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  <doc>
    <id>1108</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>28</pageNumber>
    <edition/>
    <issue>3</issue>
    <volume>7</volume>
    <articleNumber>63</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-01-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Battery Crush Test Procedures in Standards and Regulation: Need for Augmentation and Harmonisation</title>
    <abstract language="eng">Battery safety is a prominent concern for the deployment of electric vehicles (EVs). The battery powering an EV contains highly energetic active materials and flammable organic electrolytes. Usually, an EV battery catches fire due to its thermal runaway, either immediately at the time of the accident or can take a while to gain enough heat to ignite the battery chemicals. There are numerous battery abuse testing standards and regulations available globally. Therefore, battery manufacturers are always in dilemma to choose the safest one. Henceforth, to find the optimal outcome of these two major issues, six standards (SAE J2464:2009, GB/T 31485-2015:2015, FreedomCAR:2006, ISO 12405-3:2014, IEC 62660-2:2010, and SAND2017-6295:2017) and two regulations (UN/ECE-R100.02:2013 and GTR 20:2018), that are followed by more than fifty countries in the world, are investigated in terms of their abuse battery testing conditions (crush test). This research proves that there is a need for (a) augmenting these standards and regulations as they do not consider real-life vehicle crash scenarios, and (b) one harmonised framework should be developed, which can be adopted worldwide. These outcomes will solve the battery manufacturers dilemma and will also increase the safety of EV consumers.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-11083</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries7030063</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Bhavya</first_name>
      <last_name>Kotak</last_name>
    </author>
    <author>
      <first_name>Yash</first_name>
      <last_name>Kotak</last_name>
    </author>
    <author>
      <first_name>Katja</first_name>
      <last_name>Brade</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lithium-ion battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicle battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery standard</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery regulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery testing standard</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery testing regulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>abuse testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>harmonising battery standard</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>crush test procedure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery incidents</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery standard and regulation augmentation</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1108/batteries-07-00063-v2.pdf</file>
  </doc>
  <doc>
    <id>1093</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>30</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>21</volume>
    <articleNumber>1131</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-01-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">High precision outdoor and indoor reference state estimation for testing autonomous vehicles</title>
    <abstract language="eng">A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10932</identifier>
    <note>"This paper is an extended version of our paper published in : Sánchez Morales, E.; Botsch, M.; Huber, B.; García Higuera, A. High precision indoor positioning by means of LiDAR. In Proceedings of the 2019 DGON Inertial Sensors and Systems (ISS), Braunschweig, Germany, 10–11 September 2019."</note>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s21041131</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
    </author>
    <author>
      <first_name>Julian</first_name>
      <last_name>Dauth</last_name>
    </author>
    <author>
      <first_name>Bertold</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Andrés</first_name>
      <last_name>García Higuera</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>autonomous vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inertial Navigation System</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Satellite Navigation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Real-Time Kinematic</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>indoor navigation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reference state</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1093/sensors-21-01131-v2.pdf</file>
  </doc>
  <doc>
    <id>1013</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>82</pageNumber>
    <edition/>
    <issue>18</issue>
    <volume>14</volume>
    <articleNumber>5989</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2021-09-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Critical review of intelligent battery systems</title>
    <abstract language="eng">This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <subTitle language="eng">challenges, implementation, and potential for electric vehicles</subTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10132</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en14185989</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Lidiya</first_name>
      <last_name>Komsiyska</last_name>
    </author>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Buchberger</last_name>
    </author>
    <author>
      <first_name>Simon</first_name>
      <last_name>Diehl</last_name>
    </author>
    <author>
      <first_name>Moritz</first_name>
      <last_name>Ehrensberger</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hanzl</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Hartmann</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Hölzle</last_name>
    </author>
    <author>
      <first_name>Jan</first_name>
      <last_name>Kleiner</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Liebhart</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Sascha</first_name>
      <last_name>Speer</last_name>
    </author>
    <author>
      <first_name>Julia</first_name>
      <last_name>Stöttner</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Terbrack</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Hinterberger</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>smart battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>intelligent battery system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>advanced monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reconfigurable battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery management system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicle</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1013/energies-14-05989-v2-1.pdf</file>
  </doc>
  <doc>
    <id>1014</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>26</pageNumber>
    <edition/>
    <issue>14</issue>
    <volume>14</volume>
    <articleNumber>4223</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2021-07-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Future Material Developments for Electric Vehicle Battery Cells Answering Growing Demands from an End-User Perspective</title>
    <abstract language="eng">Nowadays, batteries for electric vehicles are expected to have a high energy density, allow fast charging and maintain long cycle life, while providing affordable traction, and complying with stringent safety and environmental standards. Extensive research on novel materials at cell level is hence needed for the continuous improvement of the batteries coupled towards achieving these requirements. This article firstly delves into future developments in electric vehicles from a technology perspective, and the perspective of changing end-user demands. After these end-user needs are defined, their translation into future battery requirements is described. A detailed review of expected material developments follows, to address these dynamic and changing needs. Developments on anodes, cathodes, electrolyte and cell level will be discussed. Finally, a special section will discuss the safety aspects with these increasing end-user demands and how to overcome these issues.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10149</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en14144223</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Annika</first_name>
      <last_name>Ahlberg Tidblad</last_name>
    </author>
    <author>
      <first_name>Kristina</first_name>
      <last_name>Edström</last_name>
    </author>
    <author>
      <first_name>Guiomar</first_name>
      <last_name>Hernández</last_name>
    </author>
    <author>
      <first_name>Iratxe</first_name>
      <last_name>de Meatza</last_name>
    </author>
    <author>
      <first_name>Imanol</first_name>
      <last_name>Landa-Medrano</last_name>
    </author>
    <author>
      <first_name>Jordi</first_name>
      <last_name>Jacas Biendicho</last_name>
    </author>
    <author>
      <first_name>Lluís</first_name>
      <last_name>Trilla</last_name>
    </author>
    <author>
      <first_name>Maarten</first_name>
      <last_name>Buysse</last_name>
    </author>
    <author>
      <first_name>Marcos</first_name>
      <last_name>Ierides</last_name>
    </author>
    <author>
      <first_name>Beatriz</first_name>
      <last_name>Perez Horno</last_name>
    </author>
    <author>
      <first_name>Yash</first_name>
      <last_name>Kotak</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Koch</last_name>
    </author>
    <author>
      <first_name>Bhavya</first_name>
      <last_name>Kotak</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery material</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>anode</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cathode</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electrolyte</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>end-user demands</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1014/energies-14-04223-1.pdf</file>
  </doc>
  <doc>
    <id>1018</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue>9</issue>
    <volume>167</volume>
    <articleNumber>090523</articleNumber>
    <type>article</type>
    <publisherName>IOP Publishing</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-04-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Review of Safety Aspects of Calendar Aged Lithium Ion Batteries</title>
    <abstract language="eng">Nowadays, lithium ion batteries have found their place in a various field of portable applications. With the upcoming of electrically propelled vehicles, it is mandatory to guarantee a safe and predictable behavior during the whole lifetime and not only after manufacturing during performance tests. To gain a general overview of the abuse behavior, research on the topic of abusive tests on calendar-aged cells is being investigated, split up depending on the electrode composition and lined up against each other. It can be shown, that almost all abuse experiments have been performed with external heating tests in the past, still leaving research gaps that need to be filled.</abstract>
    <parentTitle language="eng">Journal of the Electrochemical Society</parentTitle>
    <identifier type="issn">1945-7111</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10183</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1149/1945-7111/ab89bf</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian</first_name>
      <last_name>Geisbauer</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Wöhrl</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Mittmann</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1018/Geisbauer_2020_J._Electrochem._Soc._167_090523.pdf</file>
  </doc>
  <doc>
    <id>921</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>17</issue>
    <volume>14</volume>
    <articleNumber>5288</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2021-08-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Tactile Occupant Detection Sensor for Automotive Airbag</title>
    <abstract language="eng">Automotive airbags protect occupants from crash forces during severe vehicle collisions. They absorb energy and restrain the occupants by providing a soft cushion effect known as the restraint effect. Modern airbags offer partial restraint effect control by controlling the bag’s vent holes and providing multi-stage deployment. Full restraint effect control is still a challenge because the closed-loop restraint control system needs airbag–occupant contact and interaction feedback.&#13;
In this work, we have developed novel single and matrix capacitive tactile sensors to measure the occupant’s contact data. They can be integrated with the airbag surface and folded to follow the dynamic airbag shape during the deployment. The sensors are tested under a low-velocity pendulum impact and benchmarked with high-speed test videos. The results reveal that the single sensor can successfully measure occupant–airbag contact time and estimate the area, while the contact position is additionally identified from the matrix sensor.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9210</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en14175288</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Naveen</first_name>
      <last_name>Shirur</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <author>
      <first_name>Roman</first_name>
      <last_name>Henze</last_name>
    </author>
    <author>
      <first_name>Thomas Martin</first_name>
      <last_name>Deserno</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automotive airbag</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>capacitive tactile sensor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>occupant detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>passive safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vehicle crash</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/921/energies-14-05288.pdf</file>
  </doc>
  <doc>
    <id>943</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2083</pageFirst>
    <pageLast>2094</pageLast>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>51</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Chichester</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-07-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Thermomechanical stress in GaN-LEDs soldered onto Cu substrates studied using finite element method and Raman spectroscopy</title>
    <abstract language="eng">Local thermomechanical stress can cause failures in semiconductor packages during long-time operation under harsh environmental conditions. This study helps to explain the packaging-induced stress in blue GaN-LEDs soldered onto copper substrates using AuSn alloy as lead-free interconnect material. Based on the finite element method, a virtual prototype is developed to simulate the thermomechanical behavior and stress in the LED and in the complete LED/AuSn/Cu assembly considering plastic and viscoplastic strain. The investigations were performed by varying the temperature between −50°C and 180°C. To validate the model, the simulation results are compared to experimental data collected with Raman spectroscopy. Studies of the urn:x-wiley:03770486:media:jrs5947:jrs5947-math-0003 phonon mode of GaN semiconductor are elaborated to understand the induced thermomechanical stress. The model enables evaluation of the stress in the interfaces of the assembly, which otherwise cannot be accessed by measurements. It serves to predict how assemblies would perform, before committing resources to build a physical prototype.</abstract>
    <parentTitle language="eng">Journal of Raman Spectroscopy</parentTitle>
    <identifier type="issn">1097-4555</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9438</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1002/jrs.5947</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>E</first_name>
      <last_name>Liu</last_name>
    </author>
    <author>
      <first_name>Fosca</first_name>
      <last_name>Conti</last_name>
    </author>
    <author>
      <first_name>Sri Krishna</first_name>
      <last_name>Bhogaraju</last_name>
    </author>
    <author>
      <first_name>Raffaella</first_name>
      <last_name>Signorini</last_name>
    </author>
    <author>
      <first_name>Danilo</first_name>
      <last_name>Pedron</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>finite element method</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gallium nitride (GaN)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LED</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>semiconductor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>thermomechanical stress</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/943/jrs.5947.pdf</file>
  </doc>
  <doc>
    <id>926</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>140</pageFirst>
    <pageLast>146</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>10</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>De Gruyter</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-03-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">New developments on EDR (Event Data Recorder) for automated vehicles</title>
    <abstract language="eng">With the upcoming new legislative rules in the EU on Event Data Recorder beginning 2022 the question is whether the discussed data base is sufficient for the needs of clarifying accidents involving automated vehicles. Based on the reconstruction of real accidents including vehicles with ADAS combined with specially designed crash tests a broader data base than US EDR regulation (NHTSA 49 CFR Part 563.7) is proposed. The working group AHEAD, to which the authors contribute, has already elaborated a data model that fits the needs of automated driving. The structure of this data model is shown. Moreover, the special benefits of storing internal video or photo feeds form the vehicle camera systems combined with object data is illustrated. When using a sophisticate 3D measurement method of the accident scene the videos or photos can also serve as a control instance for the stored vehicle data. The AHEAD Data Model enhanced with the storage of the video and photo feeds should be considered in the planned roadmap of the Informal Working Group (IWG) on EDR/ DSSAD (Data Storage System for Automated Driving) reporting to UNECE WP29. Also, a data access over the air using technology already applied in China for electric vehicles called Real Time Monitoring would allow a quantum leap in forensic accident reconstruction.</abstract>
    <parentTitle language="eng">Open Engineering</parentTitle>
    <identifier type="issn">2391-5439</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9263</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1515/eng-2020-0007</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Böhm</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Paula</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Event Data Recorder (EDR)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>video and photo feeds</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Real Time Monitoring (RTM)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AHEAD (Aggregated Homologation proposal for Event data recorder for Automated Driving)</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/926/10.1515_eng-2020-0007.pdf</file>
  </doc>
  <doc>
    <id>951</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>198</pageFirst>
    <pageLast>208</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>63</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-03-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Teaching Battery Basics in Laboratories: Hands-On Versus Simulated Experiments</title>
    <abstract language="eng">Contribution: Prior studies comparing the effectiveness of different laboratory learning modes do not allow one to draw a universally valid conclusion, as other influences are mixed with the learning modes. In order to contribute to the existing body of work and to add another piece to the puzzle, this article demonstrates an improved methodology to evaluate the effectiveness of computer-simulated laboratories in comparison to hands-on exercises using a battery basics practical course as a case study.&#13;
&#13;
Background: Computer-simulated experiments are becoming increasingly popular for conducting laboratory exercises in higher education and vocational training institutions. To ensure the consistent quality of laboratory learning, an accurate comparison between the results of simulated experiments and practical hands-on experiments is required.&#13;
&#13;
Intended Outcomes: In this article, the achievement of the following learning objectives were compared between the two laboratory modes: 1) comprehension of the most important parameters of battery cells and 2) knowledge on how these parameters can be determined using adequate experimental procedures.&#13;
&#13;
Application Design: To avoid interference of factors other than laboratory mode on the learning, laboratory instructions and experimental interfaces ensured identical execution of the experiments in the compared modes. Using a counterbalanced methodology, the two laboratory modes alternated by the session, while the experimental procedures remained constant regardless of the respective modes.&#13;
&#13;
Findings: Tests taken by the participants after conducting the laboratory experiments revealed that hands-on laboratories resulted in statistically significantly better student performance than simulated laboratories. This difference was even more pronounced for the participants that finished a vocational education and training program before the university studies.</abstract>
    <parentTitle language="eng">IEEE Transactions on Education</parentTitle>
    <identifier type="issn">1557-9638</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9517</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/TE.2020.2970554</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Fabian</first_name>
      <last_name>Steger</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Nitsche</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Arbesmeier</last_name>
    </author>
    <author>
      <first_name>Katja</first_name>
      <last_name>Brade</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <author>
      <first_name>Iouri</first_name>
      <last_name>Belski</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electrical engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>energy storages</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>hands-on</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>knowledge gain</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>laboratory learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>student experience</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/951/Teaching_Battery_Basics_in_Laboratories.pdf</file>
  </doc>
  <doc>
    <id>1009</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>11</issue>
    <volume>14</volume>
    <articleNumber>3358</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2021-06-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparative Study on the Calendar Aging Behavior of Six Different Lithium-Ion Cell Chemistries in Terms of Parameter Variation</title>
    <abstract language="eng">The degradation of lithium-ion cells is an important aspect, not only for quality management, but also for the customer of the application like, e.g., scooters or electric vehicles. During the lifetime of the system, the overall health on the battery plays a key role in its depreciation. Therefore, it is necessary to monitor the health of the battery during operation, i.e., cycle life, but also during stationary conditions, i.e., calendar aging. In this work, the degradation due to calendar aging is analyzed for six different cell chemistries in terms of capacity degradation and impedance increase and their performance are being compared. In a new proposed metric, the relative deviations between various cells with the exact identical aging history are being analyzed for their degradation effects and their differences, which stands out in comparison to similar research. The capacity loss was found to be most drastic at 60 °C and at higher storage voltages, even for titanate-oxide cells. LiNiMnCoO2 (NMC), LiNiCoAlO2 (NCA) and Li2TiO3 (LTO) cells at 60 °C showed the most drastic capacity decrease. NMC and NCA cells at 60 °C and highest storage voltage did not show any open circuit voltage, as their current interrupt mechanism triggered. The effect of aging shows no uniform impact on the changes in the capacity variance when comparing different aging conditions, with respect to the evaluated standard deviation for all cells. The focus of this work was on the calendar aging effect and may be supplemented in a second study for cyclic aging.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10091</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en14113358</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian</first_name>
      <last_name>Geisbauer</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Wöhrl</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Koch</last_name>
    </author>
    <author>
      <first_name>Gudrun</first_name>
      <last_name>Wilhelm</last_name>
    </author>
    <author>
      <first_name>Gerhard</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electromobility</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>batteries</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lithium-ion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>calendar aging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>capacity degradation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>storage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parameter distribution</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1009/energies-14-03358-v2.pdf</file>
  </doc>
  <doc>
    <id>1011</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>8</issue>
    <volume>14</volume>
    <articleNumber>2217</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2021-04-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">End of Electric Vehicle Batteries</title>
    <abstract language="eng">It is a fact that electric vehicles (EVs) are beneficial for climate protection. However, the current challenge is to decide on whether to reuse an EV battery or to recycle it after its first use. This paper theoretically investigates these areas i.e., recycle and reuse. It was found that there are several commercially used recycling processes and also some are under research to regain maximum possible materials and quantity. The concept of reusing (second life) of the battery is promising because, at the end of the first life, batteries from EVs can be used in several applications such as storing energy generated from renewable sources to support the government grid. However, the cost and life-cycle analysis (LCA) demonstrated that there are several aspects involved in battery reuse applications. Henceforth, one LCA generalised method cannot provide an optimal approach for all cases. It is important to have a detailed study on each of the battery reusing applications. Until then, it is safe to say that reusing the battery is a good option as it would give some time to recycling companies to develop cost and energy-efficient methods.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <subTitle language="eng">Reuse vs. Recycle</subTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10115</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en14082217</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Yash</first_name>
      <last_name>Kotak</last_name>
    </author>
    <author>
      <first_name>Carlos</first_name>
      <last_name>Marchante Fernández</last_name>
    </author>
    <author>
      <first_name>Lluc</first_name>
      <last_name>Canals Casals</last_name>
    </author>
    <author>
      <first_name>Bhavya</first_name>
      <last_name>Kotak</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Koch</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Geisbauer</last_name>
    </author>
    <author>
      <first_name>Lluís</first_name>
      <last_name>Trilla</last_name>
    </author>
    <author>
      <first_name>Alberto</first_name>
      <last_name>Gómez-Nuñez</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery recycling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery reuse</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery second life</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>circular economy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lithium-ion cells</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery components recycling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sustainability in mobility</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery cost analysis</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1011/energies-14-02217.pdf</file>
  </doc>
  <doc>
    <id>1012</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>21</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>14</volume>
    <articleNumber>1040</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2021-02-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Crashed Electric Vehicle Handling and Recommendations - State of the Art in Germany</title>
    <abstract language="eng">In the near future, electric powered vehicles will represent a major part of the road traffic. Accordingly, there will be a natural increase of accidents involving electric vehicles. There are not many cases of such accidents yet and therefore the experience and correct handling are still partially open points for the involved parties, such as the rescue services for example. The aim of this work is to provide a complete overview of the accident handling sequence in Germany, starting with the damaged vehicle on site and moving on to the risks and challenges for the stakeholders, such as transport and recycling companies. Arising from the developed overview, a handling recommendation for yet undiscussed points is given. Especially, different extinguishing and deactivation methods are compared and discussed. Due to a lack of a common live-feed from battery data on site, other criteria have to be taken into account to assess the state of the battery. The wrecked vehicle—including the high voltage system—needs to be in a definite safe state at the handover to a towing service. Depending on the case, different options for securing the vehicle will be considered in this work.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10123</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en14041040</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Wöhrl</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Geisbauer</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Nebl</last_name>
    </author>
    <author>
      <first_name>Susanne</first_name>
      <last_name>Lott</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>high voltage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>recommendation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>energy storage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>thermal runaway</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fire extinguishing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>rescue service</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>handling</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1012/energies-14-01040-v2.pdf</file>
  </doc>
  <doc>
    <id>1297</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>2021</volume>
    <articleNumber>isal_a_00399</articleNumber>
    <type>conferenceobject</type>
    <publisherName>MIT Press</publisherName>
    <publisherPlace>Cambridge</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A sustainable ecosystem through emergent cooperation in multi-agent reinforcement learning</title>
    <parentTitle language="eng">Proceedings of the Artificial Life Conference 2021</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-12975</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">ALIFE 2021: The 2021 Conference on Artificial Life, online, 19.-23.07.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1162/isal_a_00399</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Fabian</first_name>
      <last_name>Ritz</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Ratke</last_name>
    </author>
    <author>
      <first_name>Thomy</first_name>
      <last_name>Phan</last_name>
    </author>
    <author>
      <first_name>Lenz</first_name>
      <last_name>Belzner</last_name>
    </author>
    <author>
      <first_name>Claudia</first_name>
      <last_name>Linnhoff-Popien</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43324">Belzner, Lenz</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1297/isal_a_00399.pdf</file>
  </doc>
  <doc>
    <id>1306</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>20</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>7</volume>
    <articleNumber>31</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Advanced monitoring and prediction of the thermal state of intelligent battery cells in electric vehicles by physics-based and data-driven modeling</title>
    <abstract language="eng">Novel intelligent battery systems are gaining importance with functional hardware on the cell level. Cell-level hardware allows for advanced battery state monitoring and thermal management, but also leads to additional thermal interactions. In this work, an electro-thermal framework for the modeling of these novel intelligent battery cells is provided. Thereby, a lumped thermal model, as well as a novel neural network, are implemented in the framework as thermal submodels. For the first time, a direct comparison of a physics-based and a data-driven thermal battery model is performed in the same framework. The models are compared in terms of temperature estimation with regard to accuracy. Both models are very well suited to represent the thermal behavior in novel intelligent battery cells. In terms of accuracy and computation time, however, the data-driven neural network approach with a Nonlinear AutoregRessive network with eXogeneous input (NARX) shows slight advantages. Finally, novel applications of temperature prediction in battery electric vehicles are presented and the applicability of the models is illustrated. Thereby, the conventional prediction of the state of power is extended by simultaneous temperature prediction. Additionally, temperature forecasting is used for pre-conditioning by advanced cooling system regulation to enable energy efficiency and fast charging.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13068</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries7020031</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Jan</first_name>
      <last_name>Kleiner</last_name>
    </author>
    <author>
      <first_name>Magdalena</first_name>
      <last_name>Stuckenberger</last_name>
    </author>
    <author>
      <first_name>Lidiya</first_name>
      <last_name>Komsiyska</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lithium-ion battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electro-thermal model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>smart cell</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>intelligent battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>temperature prediction</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1306/batteries-07-00031.pdf</file>
  </doc>
  <doc>
    <id>1308</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>7</volume>
    <articleNumber>22</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Float Current Analysis for Fast Calendar Aging Assessment of&#13;
18650 Li(NiCoAl)O2/Graphite Cells</title>
    <abstract language="eng">Float currents are steady-state self-discharge currents after a transient phase—caused by anode overhang, polarization, etc.—is accomplished. The float current is measured in this study with a standard test bench for five 18650 cells (Samsung 25R) at potentiostatic conditions while the temperature is changed in 5 K steps from 5 °C to 60 °C. The entire test is performed in about 100 days resulting in 12 measurement points per cell potential for an Arrhenius representation. The float current follows the Arrhenius law with an activation energy of about 60 kJ/mol. The capacity loss measured at reference condition shows a high correlation to the results of float currents analysis. In contrast to classical calendar aging tests, the performed float current analysis enables determining the aging rate with high precision down to at least 10 °C. Returning from higher temperatures to 30 °C reference temperature shows reducing float currents at 30 °C for increasing temperature steps that may originate from an hysteresis effect that has to be investigated in future publications.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13081</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries7020022</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Michael</first_name>
      <last_name>Theiler</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>18650</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NCA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>graphite</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>float current</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>self-discharge</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>calendar aging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Arrhenius</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1308/batteries-07-00022.pdf</file>
  </doc>
  <doc>
    <id>1309</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2335</pageFirst>
    <pageLast>2351</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>32</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modular production control using deep reinforcement learning: proximal policy optimization</title>
    <abstract language="eng">EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design.</abstract>
    <parentTitle language="eng">Journal of Intelligent Manufacturing</parentTitle>
    <identifier type="issn">1572-8145</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13092</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s10845-021-01778-z</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Classen</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modular production</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production scheduling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep reinforcement learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>proximal policy optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automotive industry</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1309/Mayer2021_ModularProductionControlUsingD.pdf</file>
  </doc>
  <doc>
    <id>1315</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue>831</issue>
    <volume>2020</volume>
    <articleNumber>012019</articleNumber>
    <type>article</type>
    <publisherName>IOP</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">FEM-based methodology for crash severity estimation in frontal crash scenarios</title>
    <abstract language="eng">With the technological development of forward-looking sensors, researchers are exploring their use not only for advanced driver assistance systems but also to gain important pre-crash information. Based on this pre-crash information, if the occupant motion inside the vehicle structure can be predicted for the oncoming crash scenario, then an optimal restraint strategy can be planned before the crash. This paper introduces a two-step FEM simulation based methodology for predicting the occupant severity in head-on crash scenarios. In the first step, we simulate the vehicle level model with different impact positions and relative approach angles. The results of these simulations, linear velocities in the longitudinal and lateral direction and angular velocities (roll, pitch, and yaw) during in-crash phase are the loading conditions for next simulation step (occupant level). This step simulates the motion of the driver in different crash scenarios. In this paper, we investigate the head, neck, and chest injury risks from vehicle-to-vehicle crash both traveling at 50 kilometers per hour. Prediction of the head injury criterion, identifying the cases where additional deployment of side-airbags and discussion of injury criteria with contour plots are the main outcome of this paper.</abstract>
    <parentTitle language="eng">IOP Conference Series: Materials Science and Engineering</parentTitle>
    <identifier type="issn">1757-899X</identifier>
    <identifier type="issn">1757-8981</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13159</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1088/1757-899X/831/1/012019</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">7th International Conference on Mechanical, Automotive and Materials Engineering (CMAME 2019), Melbourne (Australia), 08.-10.12.2019</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 3.0</licence>
    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Akshay</first_name>
      <last_name>Patel</last_name>
    </author>
    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Lugner</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26848">Brandmeier, Thomas</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <collection role="persons" number="27080">Lugner, Robert</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1315/Sequeira_2020_IOP.pdf</file>
  </doc>
  <doc>
    <id>1251</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue>23</issue>
    <volume>10</volume>
    <articleNumber>8516</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-15</completedDate>
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    <title language="eng">Comparison of Nondestructive Testing Methods for Solder, Sinter, and Adhesive Interconnects in Power and Opto-Electronics</title>
    <abstract language="eng">Reliability is one of the major requirements for power and opto-electronic devices across all segments. High operation temperature and/or high thermomechanical stress cause defects and degradation of materials and interconnects, which may lead to malfunctions with costly or even life-threatening consequences. To avoid or at least reduce failures, nondestructive testing (NDT) methods are common within development and production of power and opto-electronics. Currently, the dominating NDT methods are X-ray, scanning acoustic microscopy (SAM), and transient thermal analysis (TTA). However, they have different strengths and weaknesses with respect to materials and mechanical designs. This paper compares these NDT methods for different interconnect technologies, i.e., reflow soldering, adhesive, and sintered interconnection. While X-ray provided adequate results for soldered interfaces, inspection of adhesives and sintered interconnects was not possible. With SAM, evaluation of adhesives and sintered interconnects was also feasible, but quality depended strongly on the sample under test. TTA enabled sufficiently detailed results for all the interconnect applications. Automated TTA equipment, as the in-house developed tester used within this investigation, enabled measurement times compatible with SAM and X-ray. In the investigations, all methods revealed their pros and cons, and their selection has to depend on the sample under tests and the required analysis depth and data details. In the paper, guidelines are formulated for an appropriate decision on the NDT method depending on sample and requirements.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="issn">2076-3417</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-12518</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/app10238516</enrichment>
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    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Sri Krishna</first_name>
      <last_name>Bhogaraju</last_name>
    </author>
    <author>
      <first_name>E</first_name>
      <last_name>Liu</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reliability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nondestructive testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>power electronics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>X-ray</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>scanning acoustic microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>transient thermal analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>TTA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sintering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LED</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MOSFET</value>
    </subject>
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  <doc>
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    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <completedDate>2025-06-17</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Federated Learning via Decentralized Dataset Distillation in Resource Constrained Edge Environments</title>
    <abstract language="eng">In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-60054</identifier>
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    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
    </author>
    <author>
      <first_name>Dai</first_name>
      <last_name>Liu</last_name>
    </author>
    <author>
      <first_name>Dave Zhenyu</first_name>
      <last_name>Chen</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Carsten</first_name>
      <last_name>Trinitis</last_name>
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    <author>
      <first_name>Martin</first_name>
      <last_name>Schulz</last_name>
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    <author>
      <first_name>Alois</first_name>
      <last_name>Knoll</last_name>
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  <doc>
    <id>5988</id>
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    <publishedYear>2023</publishedYear>
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    <pageLast/>
    <pageNumber>8</pageNumber>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-13</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment</title>
    <abstract language="eng">Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data. Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59887</identifier>
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    <author>
      <first_name>Leonardo</first_name>
      <last_name>Novicki Neto</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
    </author>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Parente Ribeiro</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Icking</last_name>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
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    <collection role="persons" number="24800">Huber, Werner</collection>
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    <file>https://opus4.kobv.de/opus4-haw/files/5988/2310.03895v1.pdf</file>
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  <doc>
    <id>5987</id>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>5</pageNumber>
    <edition/>
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    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-13</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks</title>
    <abstract language="eng">The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN). The code and pre-trained weights are open-source and available on GitHub. This method generates 16 simultaneous chirps, which allows the generated data to be used for the further development of algorithms for processing radar data (filtering and clustering). This can increase the potential for data augmentation, e.g., by generating data in non-existent or safety-critical scenarios that are not reproducible in real life. In this work, the GAN was trained with radar measurements of a motorcycle and used to generate synthetic raw radar data of a motorcycle traveling in a straight line. For generating this data, the distance of the motorcycle and Gaussian noise are used as input to the neural network. The synthetic generated radar chirps were evaluated using the Frechet Inception Distance (FID). Then, the Range-Azimuth (RA) map is calculated twice: first, based on synthetic data using this GAN and, second, based on real data. Based on these RA maps, an algorithm with adaptive threshold and edge detection is used for object detection. The results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results. Thus, the proposed method in this work has shown to minimize the simulation-to-reality gap for the generation of radar data.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59872</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2308.02632</enrichment>
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    <author>
      <first_name>Eduardo</first_name>
      <last_name>Fidelis</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Herick Y. S.</first_name>
      <last_name>Ribeiro</last_name>
    </author>
    <author>
      <first_name>Pietro</first_name>
      <last_name>Campos</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Icking</last_name>
    </author>
    <author>
      <first_name>Lester</first_name>
      <last_name>Faria</last_name>
    </author>
    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
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    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
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    <id>5964</id>
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    <pageNumber>5</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <completedDate>2025-06-10</completedDate>
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    <title language="eng">Decentralized Fusion of 3D Extended Object Tracking based on a B-Spline Shape Model</title>
    <abstract language="eng">Extended Object Tracking (EOT) exploits the high resolution of modern sensors for detailed environmental perception. Combined with decentralized fusion, it contributes to a more scalable and robust perception system. This paper investigates the decentralized fusion of 3D EOT using a B-spline curve based model. The spline curve is used to represent the side-view profile, which is then extruded with a width to form a 3D shape. We use covariance intersection (CI) for the decentralized fusion and discuss the challenge of applying it to EOT. We further evaluate the tracking result of the decentralized fusion with simulated and real datasets of traffic scenarios. We show that the CI-based fusion can significantly improve the tracking performance for sensors with unfavorable perspective.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59648</identifier>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
    </author>
    <author>
      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
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    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
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  <doc>
    <id>6017</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>131, Part A</issue>
    <volume>2025</volume>
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    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-23</completedDate>
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    <title language="eng">The important role of lithiation-induced pressure change on apparent aging of lithium-ion batteries during accelerated cyclic aging tests</title>
    <abstract language="eng">In assessing the lifespan of lithium-ion batteries in laboratory conditions, the measured capacity declines during accelerated cyclic aging tests but increases when the cells are left idle. This recoverable capacity loss, known in literature as apparent aging, results from an inhomogeneous lithium distribution in the anode appearing in dV/dQ and capacity difference analysis. This study explores the origin of the apparent aging by correlating lithium distribution in the anode and recoverable capacity loss, with the lithiation induced pressure change and generated electrolyte motion during cycling. To this end, we compressed five commercial pouch lithium-ion cells (NMC/Gr) with holder stiffnesses ranging from 0.9 to 15.4 MPa/mm and offset pressure from 289 to 681 kPa and subjected them to continuous cyclic aging tests. While offset pressure had no significant impact, we observed, that holder stiffnesses over 3.3 MPa/mm induces an apparent capacity loss, which is recoverable by 35 % during a resting phase. The root cause is the so-called electrolyte motion induced salt inhomogeneity (EMSI), driven by continuous high current cycling and electrolyte motion, which is intensified by higher holder stiffness but not by offset pressure. These insights are crucial for improving the accuracy of accelerated aging tests and enhancing battery lifespan predictions.</abstract>
    <parentTitle language="eng">Journal of Energy Storage</parentTitle>
    <identifier type="issn">2352-1538</identifier>
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    <author>
      <first_name>Pablo</first_name>
      <last_name>Morales Torricos</last_name>
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    <author>
      <first_name>Sascha</first_name>
      <last_name>Berg</last_name>
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    <author>
      <first_name>Egbert</first_name>
      <last_name>Figgemeier</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
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    <collection role="persons" number="26571">Endisch, Christian</collection>
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    <file>https://opus4.kobv.de/opus4-haw/files/6017/1-s2.0-S2352152X25020365-main.pdf</file>
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    <pageFirst>729</pageFirst>
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    <publisherPlace>Setúbal</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-04-04</completedDate>
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    <title language="eng">Environment Setup and Model Benchmark of the MuFoRa Dataset</title>
    <abstract language="eng">Adverse meteorological conditions, particularly fog and rain, present significant challenges to computer vision algorithms and autonomous systems. This work presents MuFoRa a novel, controllable, and measured multimodal dataset recorded at CARISSMA’s indoor test facility, specifically designed to assess perceptual difficulties in foggy and rainy environments. The dataset bridges research gap in the public benchmarking datasets, where quantifiable weather parameters are lacking. The proposed dataset comprises synchronized data from two sensor modalities: RGB stereo cameras and LiDAR sensors, captured under varying intensities of fog and rain. The dataset incorporates synchronized meteorological annotations, such as visibility through fog and precipitation levels of rain, and the study contributes a detailed explanation of the diverse weather effects observed during data collection in the methods section. The dataset’s utility is demonstrated through a baseline evaluation example, asse ssing the performance degradation of state-of-the-art YOLO11 and DETR 2D object detection algorithms under controlled and quantifiable adverse weather conditions. The public release of the dataset (https://doi.org/10.5281/zenodo.14175611) facilitates various benchmarking and quantitative assessments of advanced multimodal computer vision and deep learning models under the challenging conditions of fog and rain.</abstract>
    <parentTitle language="eng">Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 3)</parentTitle>
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      <first_name>Islam</first_name>
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    <title language="eng">CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation&#13;
Awareness for Autonomous Driving</title>
    <abstract language="eng">Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms. However, the complex and highly dynamic nature of traffic environments presents significant challenges in accurately rendering these scenes. In this paper, we introduce a novel 4D Gaussian Splatting (4DGS) approach, which incorporates context and temporal deformation awareness to improve dynamic scene rendering. Specifically, we employ a 2D semantic segmentation foundation model to self-supervise the 4D semantic features of Gaussians, ensuring meaningful contextual embedding. Simultaneously, we track the temporal deformation of each Gaussian across adjacent frames. By aggregating and encoding both semantic and temporal deformation features, each Gaussian is equipped with cues for potential deformation compensation within 3D space, facilitating a more precise representation of dynamic scenes. Experimental results show that our method improves 4DGS's ability to capture fine details in dynamic scene rendering for autonomous driving and outperforms other self-supervised methods in 4D reconstruction and novel view synthesis. Furthermore, CoDa-4DGS deforms semantic features with each Gaussian, enabling broader applications.</abstract>
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    <author>
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    <title language="eng">Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation</title>
    <abstract language="eng">Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.</abstract>
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    <author>
      <first_name>Abinav</first_name>
      <last_name>Kalyanasundaram</last_name>
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    <author>
      <first_name>Karthikeyan</first_name>
      <last_name>Chandra Sekaran</last_name>
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    <author>
      <first_name>Philipp</first_name>
      <last_name>Stäuber</last_name>
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    <author>
      <first_name>Michael</first_name>
      <last_name>Lange</last_name>
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    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
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    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
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    <pageNumber>7</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <completedDate>2025-04-15</completedDate>
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    <title language="eng">Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors</title>
    <abstract language="eng">Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very low orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-58361</identifier>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
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    <author>
      <first_name>Qiuyu</first_name>
      <last_name>Xu</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <author>
      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
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    <language>eng</language>
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    <pageLast/>
    <pageNumber>8</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <completedDate>2025-04-15</completedDate>
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    <title language="eng">3D Extended Object Tracking based on Extruded B-Spline Side View Profiles</title>
    <abstract language="eng">Object tracking is an essential task for autonomous systems. With the advancement of 3D sensors, these systems can better perceive their surroundings using effective 3D Extended Object Tracking (EOT) methods. Based on the observation that common road users are symmetrical on the right and left sides in the traveling direction, we focus on the side view profile of the object. In order to leverage of the development in 2D EOT and balance the number of parameters of a shape model in the tracking algorithms, we propose a method for 3D extended object tracking (EOT) by describing the side view profile of the object with B-spline curves and forming an extrusion to obtain a 3D extent. The use of B-spline curves exploits their flexible representation power by allowing the control points to move freely. The algorithm is developed into an Extended Kalman Filter (EKF). For a through evaluation of this method, we use simulated traffic scenario of different vehicle models and realworld open dataset containing both radar and lidar data.</abstract>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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    <author>
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    <id>6050</id>
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    <pageNumber>13</pageNumber>
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    <completedDate>2025-07-02</completedDate>
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    <title language="eng">Assigning Calendar Aging during Open Circuit and Float Current Analysis to Anode and Cathode Aging Using Three-Electrode Cells</title>
    <abstract language="eng">Analyzing calendar aging by float current analysis, instead of solely repeating capacity tests, gains more insights into aging. However, the underlying mechanisms of the recharge current required to keep the voltage constant are not yet fully understood. Therefore, this study performs float current and additional open circuit voltage (OCV) tests on 3-electrode cells for four different voltages at 30 °C. By monitoring the potentials of the anode and the cathode over time individually, the aging rate on both electrodes and the present capacity can be calculated based on adjusting the electrode-specific OCV curves according to the voltage slippery theory. The electrode-specific aging and the capacity prediction show high agreement among both methods and the capacity tests at the end of the experiment. As a result, the capacity loss appears linear-like and is associated with the loss of active lithium mainly due to SEI formation on the anode. Additionally, a significant cathode voltage decay is observed especially for higher cell voltage. According to the literature, this is associated with cathode lithiation where the conductive salt is decomposed by inserting active lithium into the cathode masking SEI losses. Finally, OCV and float current analysis return comparable results and support present 2-electrode evaluation approaches.</abstract>
    <parentTitle language="eng">Journal of the Electrochemical Society</parentTitle>
    <identifier type="issn">1945-7111</identifier>
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    <author>
      <first_name>Qing</first_name>
      <last_name>Yu</last_name>
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    <author>
      <first_name>Iqra</first_name>
      <last_name>Kiran</last_name>
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    <author>
      <first_name>Moritz</first_name>
      <last_name>Ehrensberger</last_name>
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    <author>
      <first_name>Luiza</first_name>
      <last_name>Streck</last_name>
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    <author>
      <first_name>Thomas</first_name>
      <last_name>Roth</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Jossen</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
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    <title language="eng">The Evolution of Criticality in Deep Reinforcement Learning</title>
    <abstract language="eng">In Reinforcement Learning (RL), certain states demand special attention due to their significant influence on outcomes; these are identified as critical states. The concept of criticality is essential for the development of effective and robust policies and to improve overall trust in RL agents in real-world applications like autonomous driving. The current paper takes a deep dive into criticality and studies the evolution of criticality throughout training. The experiments are conducted on a new, simple yet intuitive continuous cliff maze environment and the Highway-env autonomous driving environment. Here, a novel finding is reported that criticality is not only learnt by the agent but can also be unlearned. We hypothesize that diversity in experiences is necessary for effective criticality quantification which is majorly driven by the chosen exploration strategy. This close relationship between exploration and criticality is studied utilizing two different strategies namely the ex ponential ε-decay and the adaptive ε-decay. The study supports the idea that effective exploration plays a crucial role in accurately identifying and understanding critical states.</abstract>
    <parentTitle language="eng">Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 3</parentTitle>
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    <title language="eng">GPUs All Grown-Up: Fully Device-Driven SpMV Using GPU Work Graphs</title>
    <abstract language="eng">Sparse matrix-vector multiplication (SpMV) is a key operation across high-performance computing, graph analytics, and many more applications. In these applications, the matrix characteristics, notably non-zero elements per row, can vary widely and impact which algorithm performs best. Thus, Graphics Processing Unit (GPU) SpMV algorithms often rely on costly preprocessing to determine what per-row algorithm to select to achieve high performance. In this work we combine SpMV preprocessing and the subsequent per-row processing on the GPU by leveraging the novel “Work Graphs” GPU programming model—initially designed for graphics applications—for dynamic on-device self-scheduling. Work Graphs allow for fine-grain dataflow execution of individual workgroups using emerging hardware and firmware support. As soon as preprocessing has generated sufficient work, workgroups of individual processing kernels are self-scheduled and executed, interleaved with those of other kernels. This improves cache locality and eliminates host interaction altogether. Across a suite of 59 sparse matrices, the best of various novel Work Graphs SpMV implementations outperforms state-of-the-art rocSPARSE “LRB” for a single SpMV by up to 7.19 × (mean: 3.35 ×, SD: 1.89). Furthermore, it achieves much more stable performance across various sparsity patterns than the rocSPARSE CSR-General algorithm, and even beats the advanced rocSPARSE CSR-Adaptive algorithm for up to 92 consecutive SpMV calculations. In addition, compared to rocSPARSE LRB, it reduces code complexity by 75%. Its memory footprint for supporting data structures is a fixed ∼ 25 MiB independent of matrix size, compared to rocSPARSE LRB’s data structures that scale with matrix size to hundreds of megabytes. Overall, this work showcases the performance potential of emerging dynamic on-device scheduling techniques for GPU compute applications.</abstract>
    <parentTitle language="eng">ISCA ´25: Proceedings of the 52nd Annual International Symposium on Computer Architecture</parentTitle>
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    <title language="eng">Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior</title>
    <abstract language="deu">Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The second scenario examined driver responses to phantom braking caused by the AEBS. As a result, the first scenario shows that none of the subjects could control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 seconds. In the second scenario, we found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.</abstract>
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    <title language="eng">No More Shading Languages: Compiling C++ to Vulkan Shaders</title>
    <abstract language="eng">Graphics APIs have traditionally relied on shading languages, however, these languages have a number of fundamental defects and limitations. By contrast, GPU compute platforms offer powerful, feature-rich languages suitable for heterogeneous compute. We propose reframing shading languages as embedded domain-specific languages, layered on top of a more general language like C++, doing away with traditional limitations on pointers, functions, and recursion, to the benefit of programmability. This represents a significant compilation challenge because the limitations of shaders are reflected in their lower-level representations. We present the Vcc compiler, which allows conventional C and C++ code to run as Vulkan shaders. Our compiler is complemented by a simple shading library and exposes GPU particulars as intrinsics and annotations. We evaluate the performance of our compiler using a selection of benchmarks, including a real-time path tracer, achieving competitive performance compared to their native CUDA counterparts.</abstract>
    <parentTitle language="eng">High-Performance Graphics - Symposium Papers</parentTitle>
    <identifier type="isbn">978-3-03868-291-2</identifier>
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      <first_name>Hugo</first_name>
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      <first_name>Michael</first_name>
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    <title language="eng">In-vivo myelin water quantification using diffusion-relaxation correlation MRI: a comparison of 1D and 2D methods</title>
    <abstract language="eng">Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion-recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion-relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. While 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.</abstract>
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    <author>
      <first_name>Sebastian</first_name>
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      <last_name>Engel</last_name>
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      <first_name>Claudio Mayrink</first_name>
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      <first_name>Carolin</first_name>
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      <first_name>Derek K.</first_name>
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      <first_name>Marion Irene</first_name>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/4644/In-vivo_myelin_water.pdf</file>
  </doc>
  <doc>
    <id>4031</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1571</pageFirst>
    <pageLast>1588</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11-12</issue>
    <volume>54</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Wien</publisherPlace>
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    <contributingCorporation/>
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    <completedDate>--</completedDate>
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    <title language="eng">In Vivo Myelin Water Quantification Using Diffusion–Relaxation Correlation MRI: A Comparison of 1D and 2D Methods</title>
    <abstract language="eng">Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion–relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.</abstract>
    <parentTitle language="eng">Applied Magnetic Resonance</parentTitle>
    <identifier type="issn">0937-9347</identifier>
    <identifier type="issn">1613-7507</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-40317</identifier>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
    </author>
    <author>
      <first_name>Maria</first_name>
      <last_name>Engel</last_name>
    </author>
    <author>
      <first_name>Emanuele</first_name>
      <last_name>Naldi</last_name>
    </author>
    <author>
      <first_name>Rodolfo</first_name>
      <last_name>Assereto</last_name>
    </author>
    <author>
      <first_name>Malwina</first_name>
      <last_name>Molendowska</last_name>
    </author>
    <author>
      <first_name>Lars</first_name>
      <last_name>Mueller</last_name>
    </author>
    <author>
      <first_name>Claudio Mayrink</first_name>
      <last_name>Verdun</last_name>
    </author>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Palombo</last_name>
    </author>
    <author>
      <first_name>Derek K.</first_name>
      <last_name>Jones</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MWF mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microstructure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Relaxometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diffusometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multi-component</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multidimensional</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multi-exponential</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiparametric</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Correlation imaging</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/4031/s00723-023-01584-1.pdf</file>
  </doc>
  <doc>
    <id>4178</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>206</pageFirst>
    <pageLast>214</pageLast>
    <pageNumber/>
    <edition/>
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    <type>conferenceobject</type>
    <publisherName>SciTePress</publisherName>
    <publisherPlace>Setúbal</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-12-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Instance Segmentation and Detection of Children to Safeguard Vulnerable Traffic User by Infrastructure</title>
    <abstract language="eng">Cameras mounted on intelligent roadside infrastructure units and vehicles can detect humans on the road using state-of-the-art perception algorithms, but these algorithms are presently not trained to distinguish between human and adult. However, this is a crucial requirement from a safety perspective because a child may not follow all the traffic rules, particularly while crossing the road. Moreover, a child may stop or may start playing on the road. In such situations, the separation of a child from an adult is necessary. The work in this paper targets to solve this problem by applying a transfer-learning-based neural network approach to classify child and adult separately in camera images. The described work is comprised of image data collection, data annotation, transfer learning-based model development, and evaluation. For the work, Mask-RCNN (region-based convolutional neural network) with different backbone architectures and two different baselines are investigated and the perception precision of the architectures after transfer-learning is compared. The results reveal that the best performing trained model is able to detect and classify children and adults separately in different road scenarios with segmentation mask AP (average precision) of 85% and bounding box AP of 92%.</abstract>
    <parentTitle language="eng">Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems</parentTitle>
    <identifier type="issn">2184-495X</identifier>
    <identifier type="isbn">978-989-758-652-1</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-41785</identifier>
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    <enrichment key="THI_conferenceName">9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023), Prague &amp; online (Czech Republic), 26.-28.04.2023</enrichment>
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    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <editor>
      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
    </editor>
    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
    </author>
    <editor>
      <first_name>Jeroen</first_name>
      <last_name>Ploeg</last_name>
    </editor>
    <author>
      <first_name>Sumit</first_name>
      <last_name>Amanagi</last_name>
    </author>
    <editor>
      <first_name>Karsten</first_name>
      <last_name>Berns</last_name>
    </editor>
    <author>
      <first_name>Kristina</first_name>
      <last_name>Doycheva</last_name>
    </author>
    <editor>
      <first_name>Oleg</first_name>
      <last_name>Gusikhin</last_name>
    </editor>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Child and Adult Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Intelligent Roadside Infrastructure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image Segmentation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mask-RCNN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Traffic Flow Optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transfer Learning</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/4178/118254.pdf</file>
  </doc>
  <doc>
    <id>5942</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>26</pageNumber>
    <edition/>
    <issue>11</issue>
    <volume>25</volume>
    <articleNumber>3422</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection</title>
    <abstract language="eng">Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-59421</identifier>
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    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5942/sensors-25-03422.pdf</file>
  </doc>
  <doc>
    <id>3046</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>28</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>23</volume>
    <articleNumber>159</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-01-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics</title>
    <abstract language="eng">A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-30464</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s23010159</enrichment>
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    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Alberto</first_name>
      <last_name>Flores Fernández</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
    </author>
    <author>
      <first_name>Andrés</first_name>
      <last_name>García Higuera</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>On-Board Diagnostics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transformer Neural Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Autonomous Driving</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ADAS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inertial Navigation Systems</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26695">Facchi, Christian</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3046/sensors-23-00159.pdf</file>
  </doc>
  <doc>
    <id>952</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>174931</pageFirst>
    <pageLast>174942</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>8</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-09-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios</title>
    <abstract language="eng">Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9528</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ACCESS.2020.3024597</enrichment>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mateus</first_name>
      <last_name>Da Rosa Zanatta</last_name>
    </author>
    <author>
      <first_name>Joao Paulo</first_name>
      <last_name>Carvalho Lustosa da Costa</last_name>
    </author>
    <author>
      <first_name>Felix</first_name>
      <last_name>Antreich</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Haardt</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <author>
      <first_name>Fábio Lúcio</first_name>
      <last_name>Lopes de Mendonça</last_name>
    </author>
    <author>
      <first_name>Rafael Timóteo</first_name>
      <last_name>de Sousa Junior</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Global navigation satellite systems (GNSS)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global positioning system (GPS)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GPS3</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>time-delay estimation (TDE)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multipath components</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model order selection (MOS)</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/952/Tensor-Based_Framework.pdf</file>
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  <doc>
    <id>2866</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>47076</pageFirst>
    <pageLast>47093</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>10</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-09-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception</title>
    <abstract language="eng">Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-28662</identifier>
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    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Quentin</first_name>
      <last_name>Delooz</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Willecke</last_name>
    </author>
    <author>
      <first_name>Keno</first_name>
      <last_name>Garlichs</last_name>
    </author>
    <author>
      <first_name>Andreas-Christian</first_name>
      <last_name>Hagau</last_name>
    </author>
    <author>
      <first_name>Lars</first_name>
      <last_name>Wolf</last_name>
    </author>
    <author>
      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Collective perception</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>information redundancy mitigation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>road safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sensor data sharing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>V2X communications</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2866/Analysis_Evaluation_Information_Redundancy.pdf</file>
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    <id>2495</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>5</volume>
    <articleNumber>12-05-04-0029</articleNumber>
    <type>article</type>
    <publisherName>SAE International</publisherName>
    <publisherPlace>Warrendale</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-07-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Dynamic Vehicle-in-the-Loop: A Novel Method for Testing Automated Driving Functions</title>
    <abstract language="eng">In automated driving functions (ADF) testing, novel methods have been developed to allow the combination of hardware and simulation to ensure safety in usage even at an early stage of development. This article proposes an architecture to integrate an entire test vehicle—denominated Dynamic Vehicle-in-the-Loop (DynViL)—in a virtual environment. This approach enables the interaction of a real vehicle with virtual traffic participants. The vehicle is physically tested on an empty track, but connected to the CARLA simulator, in which virtual driving scenarios are created. The simulated environment is transmitted to the vehicle driving function which controls the real vehicle in reaction to the virtual objects perceived in simulation. Furthermore, the performance of the DynViL in different test scenarios is evaluated. The results show that the reproducibility of the tests with the DynViL is satisfactory. Furthermore, the results indicate that the deviation between simulation and DynViL variates according to the vehicle speed and the complexity of the scenario. Based on the performance of the DynViL in comparison to simulation, the DynViL can be implemented as a complementary test method to be added to the transition between hardware in the loop (HiL) and proving ground. In this test method, erratic or unexpected behavior generated by the driving function and controllers can be detected in the real vehicle dynamics in a risk-free manner.</abstract>
    <parentTitle language="eng">SAE International Journal of Connected and Automated Vehicles</parentTitle>
    <identifier type="issn">2574-0741</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-24952</identifier>
    <identifier type="issn">2574-075X</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Varun</first_name>
      <last_name>Sharma</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Schütz</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>test</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>track</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vehicle dynamics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>virtual</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driving functions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated emergency braking</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2495/12-05-04-0029.pdf</file>
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    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
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    <volume>9</volume>
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    <publisherPlace>London</publisherPlace>
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    <completedDate>2025-04-14</completedDate>
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    <title language="eng">Laser sintering of Cu particle-free inks for high-performance printed electronics</title>
    <abstract language="eng">This study investigates laser sintering of Cu particle-free ink (Cu formate tetrahydrate—amino-2-propanol complex) as an alternative to conventional sintering in an oven (under inert/reducing atmosphere). Utilizing benefits of high-speed localized heating using laser, substrate damage can be prevented for low-melting substrates such as Polyethylene Terephthalate (PET). Firstly, a suitable sintering process window is achieved based on energy density for two different flexible polymeric susbtrates: Polyimide and PET using different laser parameters (laser power, scan rate and spot diameter). Subsequently, characterization of laser sintered traces are also made using different laser optic profiles (Gaussian and top hat). Different methodologies for fabrication of metallized Cu layer were also demonstrated. A very low bulk resistivity of 3.24 µΩcm (1.87 times of bulk Cu) was achieved on trace thickness of 0.85 ± 0.15 µm exhibiting good adherence to polymeric substrates. A promising fabrication process of low-cost and reliable flexible printed electronic devices is demonstrated.</abstract>
    <parentTitle language="eng">npj Flexible Electronics</parentTitle>
    <identifier type="issn">2397-4621</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-58266</identifier>
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    <abstract language="eng">To advance the development of multilevel inverters (MLIs) for electric vehicle applications, this study introduces novel asymmetric MLI (AMLI) configurations featuring buffer modules to enhance efficiency. A comprehensive comparative analysis of 125 distinct inverter systems is conducted, integrating five different microtopologies and 25 representative symmetrical MLI (SMLI) and AMLI systems. Unlike previous studies, this work employs a successive design process with a graph-based approach to systematically minimize conduction losses. Furthermore, experimental loss measurements ensure practical relevance. By applying Pareto optimization with cost, energy losses, and total harmonic distortion as objective functions, the study identifies the most promising MLI systems among all considered ones. While the cascaded H-bridge MLI emerges as the most cost-effective solution, specific SMLI configurations with 12 or 18 battery cells per module and AMLI designs featuring buffer modules exhibit lower inverter losses. In both cases, the incorporation of microtopologies that enable parallel operation leads to a remarkable reduction in inverter losses by more than 30 % compared to the least efficient MLI systems. Finally, a comparative assessment against a conventional B6-bridge SiC inverter highlights the significant advantages of these optimized MLI configurations in terms of efficiency and system performance.</abstract>
    <parentTitle language="eng">Energy Reports</parentTitle>
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    <title language="eng">Multi-Channel Operation for the Release 2 of ETSI Cooperative Intelligent Transport Systems</title>
    <abstract language="eng">Vehicles and road infrastructure are starting to be equipped with vehicle-to-everything (V2X) communication solutions to increase road safety and provide new services to drivers and passengers. In Europe, the deployment is based on a set of Release 1 standards developed by ETSI to support basic use cases for cooperative intelligent transport systems (C-ITS). For them, the capacity of a single 10 MHz channel in the ITS band at 5.9 GHz is considered sufficient. At the same time, the ITS stakeholders are working toward several advanced use cases, which imply a significant increment of data traffic and the need for multiple channels. To address this issue, ETSI has recently standardized a new multi-channel operation (MCO) concept for flexible, efficient, and future-proof use of multiple channels. This new concept is defined in a set of new specifications that represent the foundation for the future releases of C-ITS standards. The present article provides a comprehensive review of the new set of specifications, describing the main entities that extend the C-ITS architecture at the different layers of the protocol stack. In addition, the article provides representative examples that describe how these MCO standards will be used in the future and discusses some of the main open issues arising. The review and analysis of this article facilitate the understanding and motivation of the new set of Release 2 ETSI specifications for MCO and the identification of new research opportunities.</abstract>
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    <title language="eng">Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry</title>
    <abstract language="eng">Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data.</abstract>
    <parentTitle language="eng">Reliability Engineering &amp; System Safety</parentTitle>
    <identifier type="issn">0951-8320</identifier>
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      <first_name>Andreas</first_name>
      <last_name>Theissler</last_name>
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    <author>
      <first_name>Judith</first_name>
      <last_name>Pérez-Velázquez</last_name>
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      <first_name>Marcel</first_name>
      <last_name>Kettelgerdes</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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      <value>predictive maintenance</value>
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      <value>machine learning</value>
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      <value>condition monitoring</value>
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    <title language="eng">Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers</title>
    <abstract language="eng">Throughout a vehicle’s lifecycle, systems may fail during operation, requiring effective fault management by the vehicle controller. Various system faults affect vehicle handling differently. Additionally, vehicle velocity and road friction directly impact handling and stability. Thus, it is essential to investigate relevant factors, such as actuator faults, vehicle velocity, road friction, and their combinations, before developing a fault-tolerant controller to mitigate potential critical situations. Our work thus focuses on identifying faults and fault combinations that might lead to crashes for over-actuated vehicles during evasive maneuvers and those impacting comfort parameters. We employ a state-of-the-art vehicle controller optimized for evasive lane changes for over-actuated vehicles. The driving scenario encompasses critical conditions defined in ISO 26262 with ASIL-D, including velocities up to 130 km/h and requiring steering away from obstacles. Failure Mode and Effects Analysis, Design of Experiments, and statistical tools are used to determine fault combinations most likely to lead to crashes during evasive maneuvers. Our results indicate that the vehicle controller successfully handled the maneuver in over 53% of investigated cases, reaching up to 75.1% on dry surfaces. Road friction emerges as the most critical parameter for collision avoidance and comfort. Brake faults exhibit a higher influence on vehicle handling than other actuator faults, while single motor faults do not significantly impact vehicle parameters. Regarding two-factor interactions, brake actuators dominate, followed by steering and motor. These findings provide valuable insights for developing fault-tolerant controllers for over-actuated vehicles, guiding decisions on addressing specific faults to enhance safety and comfort parameters.</abstract>
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    <title language="eng">ResFed: Communication-Efficient Federated Learning With Deep Compressed Residuals</title>
    <abstract language="eng">Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight- or gradient-based federated learning) by over 1.4× on federated data sets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, and FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.</abstract>
    <parentTitle language="eng">IEEE Internet of Things Journal</parentTitle>
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      <first_name>Lingjuan</first_name>
      <last_name>Lyu</last_name>
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    <title language="eng">Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach</title>
    <abstract language="eng">The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.</abstract>
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      <first_name>Anupama</first_name>
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      <first_name>Rui</first_name>
      <last_name>Song</last_name>
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    <title language="eng">Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception</title>
    <abstract language="eng">Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence,&#13;
it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the&#13;
labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.</abstract>
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    <title language="eng">Cooperative Awareness Messages’ Generation Frequencies, Trigger Distributions, and Pseudonym Changes of First Commercially Deployed Vehicles in Real Operating Scenarios</title>
    <abstract language="eng">Cooperative Intelligent Transport Systems have been deployed in mass-produced vehicles in Europe (Golf 8, ID.3, etc.) since 2019, promising major improvements in vehicle safety, efficiency, and comfort. Equipped with Vehicle-to-X communication, the vehicles directly send information about their driving status to the environment. The data are already used for many Vehicle-to-X communication use-cases specified by the Car-2-Car Communication Consortium to achieve the above-mentioned improvements. This study focuses on using Vehicle-2-X communication and its messages for accident analysis. For this purpose, we investigated the advantage in temporal resolution of the continuously sent Cooperative Awareness Message compared to the Event-Data-Recorder for accident reconstruction in the pre-crash. A high resolution of the temporal accident scene is essential for the doubtless clarification of accidents. Therefore, we analyzed the generation frequencies and the trigger conditions of the Cooperative Awareness Message of vehicles available on the European market while driving in real traffic and during different parking maneuvers. Also, the pseudonym change was analyzed. We found that the average generation frequency of the Cooperative Awareness Message is higher than the 2.00 Hz recording frequency of the event data recorder for all scenarios investigated. Especially on rural roads and highways, the temporal resolution is advantageous. Since many serious accidents occur on rural roads, the use of Cooperative Awareness Messages could significantly benefit the reconstruction of the accident scene. Thus, we recommend increasing efforts to use the Cooperative Awareness Message as digital data for accident analysis and encourage more scientists to deal with this topic.</abstract>
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    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Accident analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cooperative awareness message</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cooperative intelligent transport systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fundamental data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>real operating investigation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vehicle-2-X communication</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
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    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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    <title language="eng">Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes</title>
    <abstract language="eng">Radar sensors are critical for obstacle detection and navigation, especially for automated driving. Using the use-case “printing of heating coils on the inside of the front housing (primary radome)” needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper–polyester-based filament with 6000 µΩcm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 °C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (&lt;150 °C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 µΩcm and ink 8.8 µΩcm. Using laser sintering, the ink achieved 3.2 µΩcm because the locally high temperature provides better sintering. Also, the adhesion was significantly increased to (5 B). Therefore, laser sintering is the preferred technology. In addition, it allows fast processing directly after printing. Commercial equipment is available where printing and laser sintering is integrated. The potential of low-cost copper material and the integration in additive manufacturing of electronic systems using radar sensors as an example are demonstrated in this paper.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="issn">2076-3417</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-58065</identifier>
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Using the use-case \u201cprinting of heating coils on the inside of the front housing (primary radome)\u201d needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper\u2013polyester-based filament with 6000 \u00b5\u2126cm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 \u00b0C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (&amp;lt;150 \u00b0C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 \u00b5\u2126cm and ink 8.8 \u00b5\u2126cm. Using laser sintering, the ink achieved 3.2 \u00b5\u2126cm because the locally high temperature provides better sintering. Also, the adhesion was significantly increased to (5 B). Therefore, laser sintering is the preferred technology. In addition, it allows fast processing directly after printing. Commercial equipment is available where printing and laser sintering is integrated. The potential of low-cost copper material and the integration in additive manufacturing of electronic systems using radar sensors as an example are demonstrated in this paper.&lt;\/jats:p&gt;","DOI":"10.3390\/app15052676","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T12:37:17Z","timestamp":1741005437000},"page":"2676","source":"Crossref","is-referenced-by-count":0,"title":["Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4601-979X","authenticated-orcid":false,"given":"Nihesh","family":"Mohan","sequence":"first","affiliation":[{"name":"Institute of Innovative Mobility (IIMo), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany"}]},{"given":"Fabian","family":"Steinberger","sequence":"additional","affiliation":[{"name":"Institute of Innovative Mobility (IIMo), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany"}]},{"given":"Sonja","family":"W\u00e4chter","sequence":"additional","affiliation":[{"name":"Continental Autonomous Mobility Germany GmbH, Ringlerstrasse 17, 85057 Ingolstadt, Germany"}]},{"given":"H\u00fcseyin","family":"Erdogan","sequence":"additional","affiliation":[{"name":"Continental Autonomous Mobility Germany GmbH, Ringlerstrasse 17, 85057 Ingolstadt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7643-7327","authenticated-orcid":false,"given":"Gordon","family":"Elger","sequence":"additional","affiliation":[{"name":"Institute of Innovative Mobility (IIMo), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"ref_1","unstructured":"Mallaiah, M., Thapliyal, S., and Bose, S.C. 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Advanced Driver Assistance Systems and Autonomous Vehicles from Fundamentals to Applications, Springer Nature.","DOI":"10.1007\/978-981-19-5053-7"},{"key":"ref_11","unstructured":"(2023). Road Vehicles\u2014Environmental Conditions and Testing for Electrical and Electronic Equipment, International Organization for Standardization."},{"key":"ref_12","first-page":"32","article-title":"3D Waveguide Metallized Plastic Antennas Aim to Revolutionize Automotive Radar","volume":"65","author":"Huegel","year":"2022","journal-title":"Microw. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jozwicka, M., Garcia-Tejero, A., Bekker, E., Kowalewski, J., Merli, F., and Zwick, T. (2023, January 11\u201316). 3D Printed Metallized Polymer Slotted Waveguide Antenna Array for Automotive Radar Applications at 140 GHz. 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[Bachelor\u2019s Thesis, University of Applied Science Ingolstadt]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Siah, K.S., Ramaiya, U.B., H\u00e4u\u00dfler, F., D\u00f6rfler, M., Utsch, D., Voigt, C., Franke, J., and Erdogan, H. (2024, January 15\u201319). Additively Printed Heating Structure for Radome De-Icing Application. Proceedings of the 2024 47th International Spring Seminar on Electronics Technology (ISSE), Prague, Czech Republic.","DOI":"10.1109\/ISSE61612.2024.10604114"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14495","DOI":"10.1007\/s10853-018-2645-1","article-title":"Fabrication of highly electrical conductive composite filaments for 3D-printing circuits","volume":"53","author":"Lei","year":"2018","journal-title":"J. Mater. 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Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.scriptamat.2020.02.045","article-title":"Die-Attach Bonding for High Temperature Applications Using Thermal Decomposition of Copper(II) Formate with Polyethylene Glycol","volume":"182","author":"Bhogaraju","year":"2020","journal-title":"Scr. Mater."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mohan, N., Torres, J.A., Bhogaraju, S.K., Kotadia, H., and Elger, G. (2023, January 11\u201314). Rapid Sintering of Inkjet Printed Cu Complex Inks Using Laser in Air. Proceedings of the 24th European Microelectronics and Packaging Conference &amp; Exhibition (EMPC), Cambridge, UK.","DOI":"10.23919\/EMPC55870.2023.10418323"},{"key":"ref_24","unstructured":"(2023). Standard Test Methods for Rating Adhesion by Tape Test (Standard No. ASTM D3359-23)."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.vacuum.2009.12.008","article-title":"Characterization by Optical Emission Spectroscopy of an Oxygen Plasma Used for Improving PET Wettability","volume":"84","author":"Vassallo","year":"2010","journal-title":"Vacuum"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.tsf.2013.03.067","article-title":"Control of Surface Wettability for Inkjet Printing by Combining Hydrophobic Coating and Plasma Treatment","volume":"546","author":"Park","year":"2013","journal-title":"Thin Solid Film."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/5\/2676\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T13:25:48Z","timestamp":1741008348000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/5\/2676"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,2]]},"references-count":26,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["app15052676"],"URL":"https:\/\/doi.org\/10.3390\/app15052676","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,2]]}}}</enrichment>
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      <last_name>Steinberger</last_name>
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    <title language="eng">Apparent vs. true battery aging: Impact of various load characteristics on accelerated aging tests</title>
    <abstract language="eng">Vehicle applications involve highly dynamic and variable operating conditions. These result from changing driving profiles and diverse driving behaviors. To accelerate battery aging tests, experiments are often based on constant load cycles. While efficient, this approach distorts the natural degradation behavior of the cells. This study compares cell aging under real-world and laboratory load conditions. For this purpose, we compare realistic driving profiles measured in a test vehicle in the field with synthetic constant power cycles. Average discharge power and charging sequences are kept identical for comparability. Results show that constant power cycles cause stronger cell aging than dynamic loads. Differential voltage analysis indicates more lithium inventory loss and active material loss at the negative electrode under static loads. Static loading also leads to strong inhomogeneity in the negative electrode and altered charge transfer and diffusion kinetics, as shown by impedance data. After 15 months of rest, the cells recovered up to 52  of lost capacity and 66  of the resistance increase. Impedance assessment and differential voltage analysis confirm the recovery and rehomogenization of the negative electrode. These findings show that conventional accelerated tests are strongly influenced by relaxation and load dynamics. As a result, their applicability to specific use cases, such as automotive applications, is strongly limited . Therefore, andraditional test strategies require reconsideration and redesign.</abstract>
    <parentTitle language="eng">Journal of Energy Storage</parentTitle>
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      <last_name>Morales Torricos</last_name>
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      <first_name>Marcel</first_name>
      <last_name>Rogge</last_name>
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      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
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      <first_name>Cristina</first_name>
      <last_name>Grosu</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Jossen</last_name>
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      <first_name>Markus</first_name>
      <last_name>Lienkamp</last_name>
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    <title language="eng">Float Current Analysis for Lithium-Ion Battery Aging: Insights into SEI Growth and Cathode Lithiation with EIS and ICP OES</title>
    <abstract language="eng">This study investigates calendar-aging mechanisms in lithium-ion batteries, focusing on cathode lithiation due to decomposition of conductive salt and SEI growth, by correlating quantified float currents, capacity loss rates, and pulse resistances with changes in electrochemical impedance spectroscopy (EIS) spectra. Seven SamSung 25 R cells are aged at different float voltages with periodic EIS measurements at 30 °C. Using a pre-characterization cell, the internal processes via EIS are allocated across various states of charge and temperatures and GITT measurements are performed to derive scaling factors. GITT, float currents and capacity loss rate measurements at 30 °C enables the separation of SEI growth ISEI growth and cathode lithiation current ICL based on float current behavior across a temperature range of 5 °C to 50 °C. The distribution of relaxation times (DRT) method is employed to deconvolute overlapping electrochemical processes. EIS and DRT analyses showed significant changes in cathode charge transfer resistance and diffusion, confirming that cathode lithiation correlates substantially to elevated internal resistance at high cell voltages. The theory of ISEI growth and ICL is further supported using inductively coupled plasma atomic emission spectroscopy by quantifying elemental inventory changes and linking phosphorus release and lithium consumption to degradation mechanisms.</abstract>
    <parentTitle language="eng">Journal of The Electrochemical Society</parentTitle>
    <identifier type="issn">1945-7111</identifier>
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    <author>
      <first_name>Mohamed</first_name>
      <last_name>Azzam</last_name>
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      <first_name>Atakhan</first_name>
      <last_name>Aydin</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <author>
      <first_name>Dirk Uwe</first_name>
      <last_name>Sauer</last_name>
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      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
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    <pageFirst>33792</pageFirst>
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    <completedDate>2023-04-06</completedDate>
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    <title language="eng">Coupled Lateral and Longitudinal Controller for Over-Actuated Vehicle in Evasive Maneuvering with Sliding Mode Control Strategy</title>
    <abstract language="eng">Coupled controllers are vital for safely handling vehicles, especially in critical driving situations that include changing lanes to avoid obstacles. Controllers specialized in emergencies must keep road users safe in critical situations. In this paper, we develop the coupled controller to handle evasive maneuvers for an over-actuated vehicle. The controller is based on the second-order sliding mode control theory. We use the bicycle model to establish the equivalent and robust steering equations as a control-oriented model. The lateral and longitudinal vehicle motions are coupled to each other by the lateral vehicle information on the longitudinal sliding surface, and the dependence of the lateral sliding surface on the longitudinal velocity. The torque vectoring method based on fuzzy logic adjusts the yaw moment. We address the tire slip circle on the slip controller to stabilize the vehicle while maneuvering. We simulate and evaluate our controller in a rear-end collision situation with a short time window to maneuver the vehicle. The ego vehicle detects the preceding vehicle and performs an evasive lane change while simultaneously applying brakes to bring the vehicle to a halt. Our research is the earliest in providing an ultimate emergency control to successfully avoid crashes up to 130 km/h in short time crash detection.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-34336</identifier>
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    <author>
      <first_name>Amauri</first_name>
      <last_name>da Silva Junior</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <author>
      <first_name>Reza</first_name>
      <last_name>Jazar</last_name>
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    <author>
      <first_name>Hormoz</first_name>
      <last_name>Marzbani</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>autonomous vehicle</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>crash avoidance</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>evasive maneuvers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>over-actuated vehicle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sliding mode control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vehicle coupled controllers</value>
    </subject>
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    <title language="eng">Design and Evaluation of V2X Communication Protocols for Cooperatively Interacting Automobiles</title>
    <abstract language="eng">This chapter studies two key communication services for the support of cooperative driving capabilities using Vehicle-to-Everything (V2X) communications: sensor data sharing and maneuver coordination. Based on the current state of the art in research and pre-standardization of V2X communications, we enhance the protocol design for both services and assess their performance by discrete-event simulations in highway and city scenarios. The first part of this chapter addresses the performance improvement of sensor data sharing by two complementary strategies. The shared sensor data are adapted to the available resources on the used channel. Furthermore, the redundancy of the transmitted information is reduced to lower the load on the wireless channel, whereas several approaches are proposed and assessed. The second part of the chapter analyzes cooperative maneuver coordination protocols. We propose a distributed approach based on the explicit exchange of V2X messages, which introduces priorities in maneuver coordination and studies several communication patterns for the negotiation and coordination of maneuvers among two and more vehicles. The results demonstrate the potential of V2X communications for automated driving, showcase several approaches for enhancements of sensor data sharing and maneuver coordination, and indicate the performance of these enhancements.</abstract>
    <parentTitle language="eng">Cooperatively Interacting Vehicles: Methods and Effects of Automated Cooperation in Traffic</parentTitle>
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state-of-the-art systematic review, meta-analysis and future&#13;
research directions</title>
    <abstract language="eng">Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research</abstract>
    <parentTitle language="eng">Information Fusion</parentTitle>
    <identifier type="issn">1566-2535</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13845</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.inffus.2022.01.001</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4643</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Yang</first_name>
      <last_name>Nan</last_name>
    </author>
    <author>
      <first_name>Javier</first_name>
      <last_name>Del Ser</last_name>
    </author>
    <author>
      <first_name>Simon</first_name>
      <last_name>Walsh</last_name>
    </author>
    <author>
      <first_name>Carola</first_name>
      <last_name>Schönlieb</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Roberts</last_name>
    </author>
    <author>
      <first_name>Ian</first_name>
      <last_name>Selby</last_name>
    </author>
    <author>
      <first_name>Kit</first_name>
      <last_name>Howard</last_name>
    </author>
    <author>
      <first_name>John</first_name>
      <last_name>Owen</last_name>
    </author>
    <author>
      <first_name>Jon</first_name>
      <last_name>Neville</last_name>
    </author>
    <author>
      <first_name>Julien</first_name>
      <last_name>Guiot</last_name>
    </author>
    <author>
      <first_name>Benoit</first_name>
      <last_name>Ernst</last_name>
    </author>
    <author>
      <first_name>Ana</first_name>
      <last_name>Pastor</last_name>
    </author>
    <author>
      <first_name>Angel</first_name>
      <last_name>Alberich-Bayarri</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Sean</first_name>
      <last_name>Walsh</last_name>
    </author>
    <author>
      <first_name>Wim</first_name>
      <last_name>Vos</last_name>
    </author>
    <author>
      <first_name>Nina</first_name>
      <last_name>Flerin</last_name>
    </author>
    <author>
      <first_name>Jean-Paul</first_name>
      <last_name>Charbonnier</last_name>
    </author>
    <author>
      <first_name>Eva van</first_name>
      <last_name>Rikxoort</last_name>
    </author>
    <author>
      <first_name>Avishek</first_name>
      <last_name>Chatterjee</last_name>
    </author>
    <author>
      <first_name>Henry</first_name>
      <last_name>Woodruff</last_name>
    </author>
    <author>
      <first_name>Philippe</first_name>
      <last_name>Lambin</last_name>
    </author>
    <author>
      <first_name>Leonor</first_name>
      <last_name>Cerdá-Alberich</last_name>
    </author>
    <author>
      <first_name>Luis</first_name>
      <last_name>Martí-Bonmatí</last_name>
    </author>
    <author>
      <first_name>Francisco</first_name>
      <last_name>Herrera</last_name>
    </author>
    <author>
      <first_name>Guang</first_name>
      <last_name>Yang</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Information fusion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data harmonisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data standardisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>domain adaptation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reproducibility</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1384/1-s2.0-S156625352200015X-main.pdf</file>
  </doc>
  <doc>
    <id>1331</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1306</pageFirst>
    <pageLast>1315</pageLast>
    <pageNumber/>
    <edition/>
    <issue>48</issue>
    <volume>2020</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluation and characterization of crash-pulses for head-on collisions with varying overlap crash scenarios</title>
    <abstract language="eng">Activation time for activating the occupant restraint systems (airbag and seatbelt) is very critical for an optimal safety action. A crash-pulse is the deceleration of the vehicle measured during in a crash. The shape, slope, maximum deceleration and duration of the crash-pulse provides significant information over the nature of occupant motions during in-crash phase and hence the crash severity. The above parameters of the crash-pulse not only depend on the mass and impact velocity but also on the crash configuration (position of impact, overlap, relative approach angle etc.).&#13;
&#13;
This study focuses on analysis and characterization of crash-pulses in head-on collision cases with varying overlap configurations. The paper describes causes for occupant injuries during a crash, crash-pulse and its important physical parameters, and different methodologies used to analyse the crash-pulse. Finite element simulation method is used to study the crash-pulses from different crash configurations. A new severity index that has direct influence on the occupant kinematics is defined. The results show that the steep decrease of crash-pulse for small overlap configurations (less than 25 percent of vehicle width) lags by 20 to 25 milliseconds as compared to configurations with large overlaps. The shape of the crash-pulse also changes for crash scenarios with different overlap configurations. The results, discussion and conclusion sections of this paper provide a summary of crash behaviour of varying overlap crash scenarios and insights that can be used for deployment of restraint systems.</abstract>
    <parentTitle language="eng">Transportation Research Procedia</parentTitle>
    <identifier type="issn">2352-1465</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13319</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.trpro.2020.08.156</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">World Conference on Transport Research – WCTR 2019, Mumbai (India), 26.-30.05.2019</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crash severity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Head-on collision</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Frontal crash</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crash-pulse characterization</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26848">Brandmeier, Thomas</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1331/1-s2.0-S2352146520305731-main.pdf</file>
  </doc>
  <doc>
    <id>1336</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1073</pageFirst>
    <pageLast>1088</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>14</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Hoboken</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Current commutation in a switched lithium-ion cell used in cascaded half-bridge multilevel inverters</title>
    <abstract language="eng">In order to improve the efficiency of battery electric vehicles, the utilization of reconfigurable batteries and multilevel inverters based on low-voltage Si MOSFETs gains growing attention. Focusing on the half-bridge as basic switching unit, this paper examines voltage and current waveforms during switching. It is shown that the current commutation speed depends on various factors such as sign and amplitude of the current as well as the particular switching scenario. For instance, turning off a positive cell current takes 0.21 𝜇s while turning it on takes 0.9 𝜇s. In addition, it is revealed that depending on the switching scenario and current direction, the lithium-ion cell supports or opposes the remagnetization of the parasitic inductances and therefore the switching losses of the MOSFETs. Switching a positive load current of 125 A from active to bypass converts 276 𝜇J into heat while switching from bypass to active converts merely 32.5 𝜇J into heat.</abstract>
    <parentTitle language="eng">IET Power Electronics</parentTitle>
    <identifier type="issn">1755-4543</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13364</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1049/pel2.12088</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hanzl</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Hartmann</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Hölzle</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Liebhart</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1336/IET_Power_Electronics-2021-Hanzl.pdf</file>
  </doc>
  <doc>
    <id>2952</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>9</issue>
    <volume>8</volume>
    <articleNumber>104</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Kalman Filter Tuning Using Multi-Objective Genetic Algorithm for State and Parameter Estimation of Lithium-Ion Cells</title>
    <abstract language="eng">To ensure a reliable and safe operation of battery systems in various applications, the system’s internal states must be observed with high accuracy. Hereby, the Kalman filter is a frequently used and well-known tool to estimate the states and model parameters of a lithium-ion cell. A strong requirement is the selection of a suitable model and a reasonable initialization, otherwise the algorithm’s estimation might be insufficient. Especially the process noise parametrization poses a difficult task, since it is an abstract parameter and often optimized by an arbitrary trial-and-error principle. In this work, a traceable procedure based on the genetic algorithm is introduced to determine the process noise offline considering the estimation error and filter consistency. Hereby, the parameters found are independent of the researcher’s experience. Results are validated with a simulative and experimental study, using an NCA/graphite lithium-ion cell. After the transient phase, the estimation error of the state-of-charge is lower than 0.6% and for internal resistance smaller than 4mΩ while the corresponding estimated covariances fit the error well.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29528</identifier>
    <enrichment key="opus.import.date">2022-11-11T13:18:46+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
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    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries8090104</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Michael</first_name>
      <last_name>Theiler</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Kalman filter</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>joint estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Kalman filter tuning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>genetic algorithm</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-objective optimization</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2952/batteries-08-00104-v2.pdf</file>
  </doc>
  <doc>
    <id>2929</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>49</pageFirst>
    <pageLast>54</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>figshare</publisherName>
    <publisherPlace>[s. l.]</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-10-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Real World Traffic Optimization by Reinforcement Learning: A Concept</title>
    <parentTitle language="eng">International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) Proceedings: 2022</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-29290</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.6084/m9.figshare.19733800.v1</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">2022 Agent-Based Modelling of Urban Systems (ABMUS) workshop, part of the AAMAS conference, online, 10.05.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Henri</first_name>
      <last_name>Meess</last_name>
    </author>
    <editor>
      <first_name>Minh Le</first_name>
      <last_name>Kieu</last_name>
    </editor>
    <author>
      <first_name>Jeremias</first_name>
      <last_name>Gerner</last_name>
    </author>
    <editor>
      <first_name>Koen H. van</first_name>
      <last_name>Dam</last_name>
    </editor>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Hein</last_name>
    </author>
    <editor>
      <first_name>Jason</first_name>
      <last_name>Thompson</last_name>
    </editor>
    <author>
      <first_name>Stefanie</first_name>
      <last_name>Schmidtner</last_name>
    </author>
    <editor>
      <first_name>Nick</first_name>
      <last_name>Malleson</last_name>
    </editor>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <editor>
      <first_name>Alison</first_name>
      <last_name>Heppenstall</last_name>
    </editor>
    <editor>
      <first_name>Jiaqi</first_name>
      <last_name>Ge</last_name>
    </editor>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="persons" number="43141">Schmidtner, Stefanie</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2929/Proceedings_2022_ABMUS_workshop.pdf</file>
  </doc>
  <doc>
    <id>2943</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>24</pageNumber>
    <edition/>
    <issue>11</issue>
    <volume>8</volume>
    <articleNumber>213</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-11-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Possibilities for a Quick Onsite Safety-State Assessment of Stand-Alone Lithium-Ion Batteries</title>
    <abstract language="eng">Electric vehicles’ high-voltage lithium-ion batteries are complex systems and can be sources of several hazards for interacting people. Sophisticated battery management systems (BMS) therefore constantly monitor their characteristics and varying states, to keep the battery within desired operational conditions and to mitigate safety risks as well as excessive degradation. However, there can be several situations where the battery is not in normal operation (e.g., a stand-alone battery) and a fully functional BMS monitoring function is not available. When necessary to interact with the system, its safety state must be deduced to ensure the safety of interactors. This can be a challenging task depending on a situation’s characteristics (time pressure, technical knowledge of involved people). Thus, this article discusses how the safety state of electric vehicle batteries can be evaluated quickly even by untrained people. To develop a solution, different scenarios, which require a battery’s state assessment, and the options for collecting relevant information are motivated and discussed, respectively. Finally, a mobile interface that can evaluate and display the safety state by using BMS-internal data is described and demonstrated.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29439</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries8110213</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Koch</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>safety-state assessment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>stand-alone battery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quick state assessment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery transport</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2943/batteries-08-00213.pdf</file>
  </doc>
  <doc>
    <id>2947</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>23</pageNumber>
    <edition/>
    <issue>21</issue>
    <volume>15</volume>
    <articleNumber>7902</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-11-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Calculating Available Charge and Energy of Lithium-Ion Cells Based on OCV and Internal Resistance</title>
    <abstract language="eng">The design and operation of performant and safe electric vehicles depend on precise knowledge of the behavior of their electrochemical energy storage systems. The performance of the battery management systems often relies on the discrete-time battery models, which can correctly emulate the battery characteristics. Among the available methods, electric circuit-based equations have shown to be especially useful in describing the electrical characteristics of batteries. To overcome the existing drawbacks, such as discrete-time simulations for parameter estimation and the usage of look-up tables, a set of equations has been developed in this study that solely relies on the open-circuit voltage and the internal resistance of a battery. The parameters can be obtained from typical cell datasheets or can be easily extracted via standard measurements. The proposed equations allow for the direct analytical determination of available discharge capacity and the available energy content depending on the discharge current, as well as the Peukert exponent. The fidelity of the proposed system was validated experimentally using 18650 NMC and LFP lithium-ion cells, and the results are in close agreement with the datasheet.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29479</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en15217902</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Fabian</first_name>
      <last_name>Steger</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Krogh</last_name>
    </author>
    <author>
      <first_name>Lasantha</first_name>
      <last_name>Meegahapola</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery management system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>state-of-charge</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>energy content</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mathematical model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Peukert exponent</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2947/energies-15-07902.pdf</file>
  </doc>
  <doc>
    <id>2970</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>02:1</pageFirst>
    <pageLast>02:27</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>8</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Schloss Dagstuhl</publisherName>
    <publisherPlace>Wadern</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-11-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Micro- and Macroscopic Road Traffic Analysis using Drone Image Data</title>
    <abstract language="eng">The current development in the drone technology, alongside with machine learning based image processing, open new possibilities for various applications. Thus, the market volume is expected to grow rapidly over the next years. The goal of this paper is to demonstrate the capabilities and limitations of drone based image data processing for the purpose of road traffic analysis.&#13;
In the first part a method for generating microscopic traffic data is proposed. More precisely, the state of vehicles and the resulting trajectories are estimated. The method is validated by conducting experiments with reference sensors and proofs to achieve precise vehicle state estimation results. It is also shown, how the computational effort can be reduced by incorporating the tracking information into a neural network. A discussion on current limitations supplements the findings. By collecting a large number of vehicle trajectories, macroscopic statistics, such as traffic flow and density can be obtained from the data. In the second part, a publicly available drone based data set is analyzed to evaluate the suitability for macroscopic traffic modeling. The results show that the method is well suited for gaining detailed information about macroscopic statistics, such as traffic flow dependent time headway or lane change occurrences. In conclusion, this paper presents methods to exploit the remarkable opportunities of drone based image processing for joint macro- and microscopic traffic analysis.</abstract>
    <parentTitle language="eng">Leibniz Transactions on Embedded Systems</parentTitle>
    <identifier type="issn">2199-2002</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29704</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.4230/LITES.8.1.2</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Friedrich</first_name>
      <last_name>Kruber</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Egolf</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Wurst</last_name>
    </author>
    <author>
      <first_name>Samarjit</first_name>
      <last_name>Chakraborty</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>traffic data analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>trajectory data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>drone image data</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2970/lites-v008-i001-a002-kruber.pdf</file>
  </doc>
  <doc>
    <id>1386</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>77</issue>
    <volume>2022</volume>
    <articleNumber>102387</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning residual motion correction for fast and robust 3D multiparametric MRI</title>
    <abstract language="eng">Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-13861</identifier>
    <identifier type="issn">1361-8423</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.media.2022.102387</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Jan W.</first_name>
      <last_name>Kurzawski</last_name>
    </author>
    <author>
      <first_name>Diana</first_name>
      <last_name>Waldmannstetter</last_name>
    </author>
    <author>
      <first_name>Hongwei</first_name>
      <last_name>Li</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiparametric MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D Motion correction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Residual learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiscale CNN</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1386/1-s2.0-S1361841522000391-main.pdf</file>
  </doc>
  <doc>
    <id>1388</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1831</pageFirst>
    <pageLast>1851</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>63</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Accelerated 3D whole-brain T1, T2, and proton density mapping</title>
    <abstract language="eng">Purpose: Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values. Methods: To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II–IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue. Results: The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm3 voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm3 FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency. Conclusion: 3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints.</abstract>
    <parentTitle language="eng">Neuroradiology</parentTitle>
    <subTitle language="eng">feasibility for clinical glioma MR imaging</subTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-13885</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s00234-021-02703-0</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Laura</first_name>
      <last_name>Nunez-Gonzalez</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Kofler</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
    </author>
    <author>
      <first_name>Lioba</first_name>
      <last_name>Grundl</last_name>
    </author>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</last_name>
    </author>
    <author>
      <first_name>Rolf F.</first_name>
      <last_name>Schulte</last_name>
    </author>
    <author>
      <first_name>Marion</first_name>
      <last_name>Smits</last_name>
    </author>
    <author>
      <first_name>Benedikt</first_name>
      <last_name>Wiestler</last_name>
    </author>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Juan A.</first_name>
      <last_name>Hernandez-Tamames</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image-based biomarkers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiparametric imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Glioma imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1388/Pirkl2021_Article_Accelerated3D.pdf</file>
  </doc>
  <doc>
    <id>1476</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>4</pageNumber>
    <edition/>
    <issue/>
    <volume>80</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>TU Berlin</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Congestion Aware Objects Filtering for Collective Perception</title>
    <abstract language="eng">This paper addresses collective perception for connected and automated driving. It proposes the adaptation of filtering rules based on the currently available channel resources, referred to as Enhanced DCC-Aware Filtering (EDAF).</abstract>
    <parentTitle language="eng">Electronic Communications of the EASST</parentTitle>
    <identifier type="issn">1863-2122</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-14761</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">Conference on Networked Systems 2021 (NetSys 2021): International Workshop on Communication Technologies for Vehicles (Nets4Cars), online, 13.-16.09.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">http://dx.doi.org/10.14279/tuj.eceasst.80.1160</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Quentin</first_name>
      <last_name>Delooz</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>V2X</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Decentralized Congestion Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Collective Perception</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1476/1160-3570-1-PB.pdf</file>
  </doc>
  <doc>
    <id>1971</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>129</issue>
    <volume>2022</volume>
    <articleNumber>114461</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-03-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks</title>
    <abstract language="eng">The thermo-mechanical fatigue of different SAC+ solders is investigated using transient thermal analysis (TTA) and predicted using artificial neural networks (ANN). TTA measures the thermal impedance and allows detection of solder cracks and delamination of material interfaces. LEDs soldered to printed circuit boards using seven different solders were aged within passive air-to-air temperature shock tests with TTA measurements every 50 cycles with the increase of the thermal resistance as failure criterium. A SnAgCuSb solder showed the best performance improvement over the SAC305 reference under the test conditions. In addition to standard evaluation by the cumulative failure-curve and Weibull plot, new approaches for reliability assessment are investigated to assess the reliability of the solder joint of the individual LEDs. A hybrid approach to predict failures in the solder joints of the individual LEDs during accelerated stress testing is set-up which processes the TTA data using artificial neural networks with memory, specifically LSTM, where the memory allows full use of the measurement history. Two ANN approaches, regression and classification, are used. Both approaches are shown to be quite accurate. The greater information gained from the regression approach requires more processing using external knowledge of the problem requirements, whereas the categorical approach can be more directly implemented. The results demonstrate the advantages of integrated approaches for assessment of the remaining useful life of solder joints.</abstract>
    <parentTitle language="eng">Microelectronics Reliability</parentTitle>
    <identifier type="issn">0026-2714</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-19719</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.microrel.2021.114461</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Zippelius</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Hanss</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Judith</first_name>
      <last_name>Pérez-Velázquez</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LSTM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reliability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Solder joints</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transient thermal analysis</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="persons" number="26880">Schmid, Maximilian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1971/1-s2.0-S0026271421004868-main.pdf</file>
  </doc>
  <doc>
    <id>2347</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>9</issue>
    <volume>22</volume>
    <articleNumber>3562</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-05-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">C-ITS relevant critical vehicle-to-vehicle accident scenarios for accident analysis</title>
    <abstract language="eng">The relevance of scientific investigations, whether simulative or empirical, is strongly related to the environment used and the scenarios associated with it. Within the field of cooperative intelligent transport systems, use-cases are defined to describe the benefits of applications. This has already been conducted in the available safety-relevant Day 1 applications longitudinal and intersection collision risk warning through the respective technical specifications. However, the relevance of traffic scenarios is always a function of accident severity and frequency of a retrospective consideration of accident databases. In this study, vehicle-to-vehicle scenarios with high frequency and/or severe personal injuries are therefore determined with the help of the CISS database and linked to the use-cases of the safety-relevant Day 1 applications. The relevance of the scenarios thus results on the one hand from the classical parameters of retrospective accident analysis and on the other hand from the coverage by the named vehicle-to-x applications. As a result, accident scenarios with oncoming vehicles are the most relevant scenarios for investigations with cooperative intelligent transport systems. In addition, high coverage of the most critical scenarios within the use-cases of longitudinal and intersection collision risk warning is already apparent.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-23473</identifier>
    <note>This article belongs to the Special Issue "Sensors and Systems for Automotive and Road Safety"</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s22093562</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Böhm</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Lothar</first_name>
      <last_name>Wech</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cooperative intelligent transportation systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>C-ITS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>V2X</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident scenarios</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident database</value>
    </subject>
    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="persons" number="25383">Wech, Lothar</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2347/sensors-22-03562.pdf</file>
  </doc>
  <doc>
    <id>2434</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>24</pageNumber>
    <edition/>
    <issue>12</issue>
    <volume>22</volume>
    <articleNumber>4498</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-06-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction</title>
    <abstract language="eng">The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-24341</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s22124498</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Alberto</first_name>
      <last_name>Flores Fernández</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Wurst</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
    </author>
    <author>
      <first_name>Andrés</first_name>
      <last_name>García Higuera</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PROMOTING</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>autonomous vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-modal</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>motion prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>route prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>real traffic data</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26695">Facchi, Christian</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2434/sensors-22-04498.pdf</file>
  </doc>
  <doc>
    <id>2513</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>453</pageFirst>
    <pageLast>467</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>12</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>De Gruyter</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-07-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Determination of accident scenarios via freely available accident databases</title>
    <abstract language="eng">The derivation of real accident scenarios from accident databases represents an important task within vehicle safety research. Simulations are increasingly used for this purpose. Depending on the research interest, a wide range of accident databases exists worldwide, which differ mainly in the number of recorded data per accident and availability. This work aims to identify critical vehicle-to-vehicle accidents based on freely available accident databases to derive concrete scenarios for a subsequent simulation. For this purpose, the method of the pre-crash matrix is applied using the example of the freely available Crash Investigation Sampling System database of the National Highway Traffic Safety Administration. An analysis of existing databases worldwide shows that this is the most detailed, freely available database. The derivation of scenarios succeeds here by a new method, whereby a center of gravity calculation is carried out based on the damages of the vehicles according to Collision Deformation Classification nomenclature. In addition, the determination of other necessary parameters, as well as the limits of the database, is shown in order to derive a scenario that can be simulated. As a result, the constellations of the five most frequent vehicle-to-vehicle accident scenarios according to the Crash Investigation Sampling System database are presented. In particular, other institutions should follow National Highway Traffic Safety Administration’s example and make data freely available for accident research.</abstract>
    <parentTitle language="eng">Open Engineering</parentTitle>
    <identifier type="issn">2391-5439</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-25137</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1515/eng-2022-0047</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Katrin</first_name>
      <last_name>Lecheler</last_name>
    </author>
    <author>
      <first_name>Lothar</first_name>
      <last_name>Wech</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Böhm</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Paula</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident databases</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident scenarios</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vehicle-to-vehicle accidents</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pre-Crash-Matrix</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crash Investigation Sampling System</value>
    </subject>
    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="persons" number="25383">Wech, Lothar</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2513/10.1515_eng-2022-0047.pdf</file>
  </doc>
  <doc>
    <id>2746</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>22</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>10 (2022)</volume>
    <articleNumber>09-10-02-0009</articleNumber>
    <type>article</type>
    <publisherName>SAE International</publisherName>
    <publisherPlace>Warrendale</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Crash Pulse Prediction Using Regression Algorithm with Gradient Descent Optimization Method for Integrated Safety Systems</title>
    <parentTitle language="eng">SAE International journal of transportation safety</parentTitle>
    <identifier type="issn">2327-5634</identifier>
    <identifier type="issn">2327-5626</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27466</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.4271/09-10-02-0009</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Anudeep Reddy</first_name>
      <last_name>Konda</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Lugner</last_name>
    </author>
    <author>
      <first_name>Ulrich</first_name>
      <last_name>Jumar</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crashworthiness</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Integrated-safety systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crash-pulse</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Regression</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26848">Brandmeier, Thomas</collection>
    <collection role="persons" number="27080">Lugner, Robert</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2746/09-10-02-0009.pdf</file>
  </doc>
  <doc>
    <id>2995</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue>24</issue>
    <volume>22</volume>
    <articleNumber>9832</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-12-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Does Vehicle-2-X Radio Transmission Technology Need to Be Considered within Accident Analysis in the Future?</title>
    <abstract language="eng">In this analysis, Cooperative Intelligent Transportation System relevant scenarios are created to investigate the need to differentiate Vehicle-to-X transmission technologies on behalf of accident analysis. For each scenario, the distances between the vehicles are calculated 5 s before the crash. Studies on the difference between Dedicated Short-Range Communication (IEEE 802.11p) and Cellular Vehicle-to-X communication (LTE-V2C PC5 Mode 4) are then used to assess whether both technologies have a reliable connection over the relevant distance. If this is the case, the transmission technology is of secondary importance for future investigations on Vehicle-to-X communication in combination with accident analysis. The results show that studies on freeways and rural roads can be carried out independently of the transmission technology and other boundary conditions (speed, traffic density, non-line of sight/line of sight). The situation is different for studies in urban areas, where both technologies may not have a sufficiently reliable connection range depending on the traffic density.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29954</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s22249832</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Helmer</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cooperative intelligent transportation systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>C-ITS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>C-V2X</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LTE-V2X</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DSRC</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IEEE 802.11p</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident scenarios</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident reconstruction</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="persons" number="47563">Helmer, Thomas</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2995/sensors-22-09832.pdf</file>
  </doc>
  <doc>
    <id>2998</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>25</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>20</volume>
    <articleNumber>17</articleNumber>
    <type>article</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-12-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">XEngine: Optimal Tensor Rematerialization for Neural Networks in Heterogeneous Environments</title>
    <abstract language="eng">Memory efficiency is crucial in training deep learning networks on resource-restricted devices. During backpropagation, forward tensors are used to calculate gradients. Despite the option of keeping those dependencies in memory until they are reused in backpropagation, some forward tensors can be discarded and recomputed later from saved tensors, so-called checkpoints. This allows, in particular, for resource-constrained heterogeneous environments to make use of all available compute devices. Unfortunately, the definition of these checkpoints is a non-trivial problem and poses a challenge to the programmer—improper or excessive recomputations negate the benefit of checkpointing.&#13;
&#13;
In this article, we present XEngine, an approach that schedules network operators to heterogeneous devices in low memory environments by determining checkpoints and recomputations of tensors. Our approach selects suitable resources per timestep and operator and optimizes the end-to-end time for neural networks taking the memory limitation of each device into account. For this, we formulate a mixed-integer quadratic program (MIQP) to schedule operators of deep learning networks on heterogeneous systems. We compare our MIQP solver XEngine against Checkmate [12], a mixed-integer linear programming (MILP) approach that solves recomputation on a single device. Our solver finds solutions that are up to 22.5% faster than the fastest Checkmate schedule in which the network is computed exclusively on a single device. We also find valid schedules for networks making use of both central processing units and graphics processing units if memory limitations do not allow scheduling exclusively to the graphics processing unit.</abstract>
    <parentTitle language="eng">ACM Transactions on Architecture and Code Optimization</parentTitle>
    <identifier type="issn">1544-3973</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29989</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1145/3568956</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY-NC-SA 4.0</licence>
    <author>
      <first_name>Manuela</first_name>
      <last_name>Schuler</last_name>
    </author>
    <author>
      <first_name>Richard</first_name>
      <last_name>Membarth</last_name>
    </author>
    <author>
      <first_name>Philipp</first_name>
      <last_name>Slusallek</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>rematerialization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>integer linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>memory management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>heterogeneous computing</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43674">Membarth, Richard</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2998/3568956.pdf</file>
  </doc>
  <doc>
    <id>3067</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue>205</issue>
    <volume>2022</volume>
    <articleNumber>112163</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-01-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pareto optimization of wavelet filter design for partial discharge detection in electrical machines</title>
    <abstract language="eng">A reliable detection of partial discharges (PDs) in the insulation system of electrical machines is essential to prevent premature breakdowns. Wavelet filters (WFs) are often recommended for denoising this PD measurement data. However, filtering performance strongly depends on numerous settings, which impedes an optimum filter choice. We propose an approach to systematically select the best filter for denoising PD signals. First, representative PD pulses are determined from measurement data by an averaging and a singular value decomposition method. Next, realistic benchmark signals are generated for filter assessment. Further, all possible WF combinations from a set of different mother wavelet selection methods, decomposition trees, and thresholding approaches are evaluated. On the basis of two contradicting objectives, namely mean square error of noise intervals and pulse amplitude distortion, Pareto optimization is applied to choose optimal WF settings. The corresponding filters dominate several common WF proposals from previous literature.</abstract>
    <parentTitle language="eng">Measurement</parentTitle>
    <identifier type="issn">1873-412X</identifier>
    <identifier type="issn">0263-2241</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-30673</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.measurement.2022.112163</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Rauscher</last_name>
    </author>
    <author>
      <first_name>Manuel</first_name>
      <last_name>Hufnagel</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pareto optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wavelet filter</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Denoising</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ultra high frequency</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Partial discharge</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Electrical machine</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3067/1-s2.0-S0263224122013598-main.pdf</file>
  </doc>
  <doc>
    <id>3515</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>26</pageNumber>
    <edition/>
    <issue>8</issue>
    <volume>23</volume>
    <articleNumber>3901</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis of Deactivation of 18,650 Lithium-Ion Cells in CaCl2, Tap Water and Demineralized Water for Different Insertion Times</title>
    <abstract language="eng">The deployment of battery-powered electric vehicles in the market has created a naturally increasing need for the safe deactivation and recycling of batteries. Various deactivating methods for lithium-ion cells include electrical discharging or deactivation with liquids. Such methods are also useful for cases where the cell tabs are not accessible. In the literature analyses, different deactivation media are used, but none include the use of calcium chloride (CaCl2) salt. As compared to other media, the major advantage of this salt is that it can capture the highly reactive and hazardous molecules of Hydrofluoric acid. To analyse the actual performance of this salt in terms of practicability and safety, this experimental research aims to compare it against regular Tap Water and Demineralized Water. This will be accomplished by performing nail penetration tests on deactivated cells and comparing their residual energy against each other. Moreover, these three different media and respective cells are analysed after deactivation, i.e., based on conductivity measurements, cell mass, flame photometry, fluoride content, computer tomography and pH value. It was found that the cells deactivated in the CaCl2 solution did not show any signs of Fluoride ions, whereas cells deactivated in TW showed the emergence of Fluoride ions in the 10th week of the insertion. However, with the addition of CaCl2 in TW, the deactivation process &gt; 48 h for TW declines to 0.5–2 h, which could be an optimal solution for real-world situations where deactivating cells at a high pace is essential.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-35154</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s23083901</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Wöhrl</last_name>
    </author>
    <author>
      <first_name>Yash</first_name>
      <last_name>Kotak</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Geisbauer</last_name>
    </author>
    <author>
      <first_name>Sönke</first_name>
      <last_name>Barra</last_name>
    </author>
    <author>
      <first_name>Gudrun</first_name>
      <last_name>Wilhelm</last_name>
    </author>
    <author>
      <first_name>Gerhard</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lithium-ion cells</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cell deactivation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>18,650 lithium-ion cells</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cell deactivation in tap water</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cell deactivation in demineralized water</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cell deactivation in CaCl2 solution</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3515/sensors-23-03901-v2.pdf</file>
  </doc>
  <doc>
    <id>3547</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>9</volume>
    <articleNumber>230</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Apparent Aging during Accelerated Cycling Aging Test of Cylindrical Silicon Containing Li-Ion Cells</title>
    <abstract language="eng">Accelerated cyclic aging tests are very important for research and industry to quickly characterize lithium-ion cells. However, the accentuation of stress factors and the elimination of rest periods lead to an apparent capacity fade, that can be subsequently recovered during a resting phase. This effect is attributed to the inhomogeneous lithium distribution in the anode and is observable with differential voltage analysis (DVA). We tested cylindrical 18,650 cells with Li(NixCoyAlz)O2-graphite/silicon chemistry during two cycling and resting phases. The capacity, the pulse resistance, the DVA, and the capacity difference analysis are evaluated for cells cycled at different average SOC and current rates. An apparent capacity loss of up to 12% was reported after 200 FCE for cells cycled under the presence of pressure gradients, while only 1% were at low-pressure gradients. The subsequent recovery was up to 80% of the apparent capacity loss in some cases. The impact of silicon cannot be estimated as it shows no features in the dV/dQ curves. We observe a recovery of apparent resistance increase, which is not reported for cells with pure graphite anodes. Finally, we demonstrate the strong impact of apparent aging for the lifetime prediction based on standard accelerated cyclic aging tests.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-35477</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries9040230</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Pablo</first_name>
      <last_name>Morales Torricos</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>aging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lifetime prognosis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cycle life</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NCA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>silicon</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mechanical stress</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>homogeneity of lithium distribution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>capacity recovery</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3547/batteries-09-00230.pdf</file>
  </doc>
  <doc>
    <id>3799</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>23</pageNumber>
    <edition/>
    <issue>14</issue>
    <volume>12</volume>
    <articleNumber>3130</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Control and Implementation of the Parallel Enhanced Commutation Integrated Nested Multilevel Inverter Topology</title>
    <abstract language="eng">Due to their high efficiency and advanced battery management capability, cascaded multilevel inverters are an exciting option for battery electric powertrains. A promising, new and highly efficient cascaded multilevel inverter is the Parallel Enhanced Commutation Integrated Nested Multilevel Inverter. The inverter, with four semiconductor switches per submodule, can reconfigure individual battery cells in series and parallel and generate positive and negative phase voltages in regular four-quadrant operation. Therefore, emerging degrees of freedom in battery management and inverter operation must be managed and mapped into a specific Switching State for every switch. As controlling the high number of switches is safety-relevant, this publication profoundly explains the inverter’s functionality. We introduce a Switching Function that accepts easy-to-understand functional states as input, simplifying research on higher-level control algorithms and advanced single-cell battery-management capabilities. As the Switching Function guarantees safe operation and the correct contribution of every cell to the overall functionality of the inverter, it enables researchers to confidently use and thereby accelerate research on the promising new topology. The method we describe is fast, simple, deterministic and designed to convert setpoint specifications into an executable Switching Pattern. We prove that our Switching Function is operable on an FPGA with a twenty-kilohertz setpoint update operating a 17-level inverter.</abstract>
    <parentTitle language="eng">Electronics</parentTitle>
    <identifier type="issn">2079-9292</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-37990</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/electronics12143130</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_DownloadUrl">https://www.mdpi.com/article/10.3390/electronics12143130/s1</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Terbrack</last_name>
    </author>
    <author>
      <first_name>Sascha</first_name>
      <last_name>Speer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cascaded</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>drivetrain</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electric vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PECIN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>power electronics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reconfigurable battery system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Switching Function</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3799/electronics-12-03130.pdf</file>
  </doc>
  <doc>
    <id>3669</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue>9</issue>
    <volume>16</volume>
    <articleNumber>3889</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-05-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Long-Term Self-Discharge Measurements and Modelling for Various Cell Types and Cell Potentials</title>
    <abstract language="eng">Self-discharge of lithium-ion cells leads to voltage decay over time. In this work, the self-discharge was measured at 30 °C for three cell types at various voltage levels for about 150 days in a constant voltage mode determining the current at a high precision (float current). All cells exhibit a transient part leading to a steady-state, which is no longer influenced by reversible effects. To study the effect of the check-ups on the steady-state float current, the cells, interrupted and not interrupted by check-ups, were compared. The results indicate that both the transient processes and steady-state currents are highly reproducible. In the first period of the float current, the polarization dominates the measured current, followed by the anode overhang effect dominating the process for a period of 5–30 days. After the decline of both processes, a mostly constant steady-state in the order of µA is observed. The check-up interruption generally shows no apparent effect on the reached steady-state and results only in an extended settling time. A model to simulate the transient process and steady-state of float currents was developed. The model shows a high accuracy in reproducing the results and identifying the time needed to reach the steady-state.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-36693</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en16093889</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mohamed</first_name>
      <last_name>Azzam</last_name>
    </author>
    <author>
      <first_name>Moritz</first_name>
      <last_name>Ehrensberger</last_name>
    </author>
    <author>
      <first_name>Reinhard</first_name>
      <last_name>Scheuer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>18650</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NCA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NMC</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LFP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>graphite</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>float current analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>potentiostatic hold</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>self-discharge rate</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>filtering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>anode overhang</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3669/energies-16-03889.pdf</file>
  </doc>
  <doc>
    <id>1409</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>11</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IACSIT Press</publisherName>
    <publisherPlace>Singapur</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sampling Algorithms Combination with Machine Learning for Effcient Safe Trajectory Planning</title>
    <abstract language="eng">The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources.</abstract>
    <parentTitle language="eng">International Journal of Machine Learning and Computing</parentTitle>
    <identifier type="issn">2010-3700</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-14099</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.18178/ijmlc.2021.11.1.1007</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Amit</first_name>
      <last_name>Chaulwar</last_name>
    </author>
    <author>
      <first_name>Hussein</first_name>
      <last_name>Al-Hashimi</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Safe trajectory planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>hybrid machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>collision avoidance and mitigation</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1409/1007-ICITE19-387.pdf</file>
  </doc>
  <doc>
    <id>2286</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>8</volume>
    <articleNumber>33</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-04-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of Switching on the Aging of High Power Lithium-Ion Cells</title>
    <abstract language="eng">For intelligent battery systems that are able to control the current flow for each individual cell, the multilevel inverter is an interesting approach to replace the bidirectional AC/DC-converter and improve flexibility of charging system and signal quality in both directions. Therefore, the cells are modulated by switching varying the duty cycle, the current and the frequency up to the kHz-range. This is only beneficial if the switching does not lead to a significant additional aging. The scientific gap to assess and understand the impact of switching is investigated in this paper by testing 22 high-power 18650 lithium-ion cells (Samsung 25R). The cells are tested at 50 Hz and 10 kHz switching frequency during charge, discharge and charge/discharge at 50% duty cycle. The tests are compared to eight reference tests with continuous current flow performed at the average and the maximum current for charge and discharge, respectively. The results are obtained by evaluating the remaining capacity, resistance, electrochemical impedance spectroscopy and dV/dQ analysis. Before reaching rollover, the investigated cells lose homogeneity and cathode capacity but no significant difference for the aging parameters are found. After rollover, the cell-to-cell variation is greater than the aging induced by the different cycling parameters.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-22861</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries8040033</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Guy</first_name>
      <last_name>Williams Ngaleu</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Theiler</last_name>
    </author>
    <author>
      <first_name>Xenia</first_name>
      <last_name>Straßer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hanzl</last_name>
    </author>
    <author>
      <first_name>Lidiya</first_name>
      <last_name>Komsiyska</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>18650</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NCA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>graphite</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>switching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multilevel inverter</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>frequency</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cycle life</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2286/batteries-08-00033.pdf</file>
  </doc>
  <doc>
    <id>3198</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>11</volume>
    <articleNumber>501</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking</title>
    <abstract language="eng">Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.</abstract>
    <parentTitle language="eng">Processes</parentTitle>
    <identifier type="issn">2227-9717</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31989</identifier>
    <enrichment key="opus.import.date">2023-03-08T03:33:58+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">5b125bc57f440831dbfd41ab1ec9e7f4</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/pr11020501</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Numan</first_name>
      <last_name>Senel</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
    </author>
    <author>
      <first_name>Kristina</first_name>
      <last_name>Doycheva</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>environmental perception</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sensor fusion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>autonomous vehicle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>unscented Kalman filter</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>object tracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>roadside units</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3198/processes-11-00501-v3.pdf</file>
  </doc>
  <doc>
    <id>3111</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>3</issue>
    <volume>9</volume>
    <articleNumber>145</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Experimental Investigation of State and Parameter Estimation within Reconfigurable Battery Systems</title>
    <abstract language="eng">The battery system is one of the most-important, but also -critical components in the electric power-train. The battery’s system states and parameters are commonly tracked by the battery monitoring system. However, in reality, the accuracy of the state and parameter estimation may suffer from insufficient excitation of the system. Since the current states and parameters serve as the basis for many battery management system functions, this might lead to incorrect operation and severe damage. Reconfigurable battery systems allow enhancing the system’s excitation by applying a switching operation. In this contribution, the state and parameter estimation of a reconfigurable battery module were simulated and tested experimentally. Thereby, a low-exciting and a high-exciting drive cycle were compared. Furthermore, the switching patterns were applied to enhance the excitation and, hence, improve the estimation of an extended Kalman filter. The cells were switched via a pulse-width modulation signal, and the influence of frequency and duty cycle variation on the estimation accuracy were investigated. Compared to the low-excitation input, a significant improvement in the estimation of up to 46% for the state of charge and 78% for the internal resistance were achieved. Hereby, low frequencies and duty cycles proved to be particularly advantageous. Switching, however, has only a limited influence on an already highly excited system and may lead to additional aging due to higher heat generation.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31110</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries9030145</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Michael</first_name>
      <last_name>Theiler</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>battery model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Kalman filter</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>joint estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reconfigurable battery systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>state estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parameter estimation</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3111/batteries-09-00145.pdf</file>
  </doc>
  <doc>
    <id>3967</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>378</pageFirst>
    <pageLast>388</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>29</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">3D simulations and laboratory experiments to evaluate a dynamic airbag valve</title>
    <abstract language="eng">Airbag pressure determines the restraint effect during a vehicle crash. The pressure required to restrain the occupant depends on pre-crash detection, collision parameters and the occupant’s mass and position. This work modulated airbag pressure for optimum safety using a novel airbag control valve for cold-gas inflators. This paper evaluates the valve’s stationary and dynamic performances for Helium by 3D flow simulations using a pressure-based solver in ANSYS Fluent® and SAE J2238 laboratory tank tests. The predicted and measured tank pressures for the fully open (stationary) valve were agreed by an average 93.73% with an excellent correlation (correlation coefficient, R = 0.9995). For the first dynamic operation with 10 ms switching time, the results agreed by 92.78% with R = 0.9975. In the second test with 30 ms switching, 83.67% agreement was observed with R = 0.9893. The research concluded that the valve modulates the bag pressure and is implementable in vehicles.</abstract>
    <parentTitle language="eng">International Journal of Crashworthiness</parentTitle>
    <identifier type="issn">1754-2111</identifier>
    <identifier type="issn">1358-8265</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-39673</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
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Method for operating a pre-crash sensing system to deploy airbags using inflation control. Google Patents, 2013."},{"key":"CIT0009","volume-title":"Continuously predicting crash severity","author":"Sala DM","year":"2003"},{"key":"CIT0010","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2010.07.013"},{"key":"CIT0011","unstructured":"Wood R. Characterisation of particulate matter originating from automotive occupant restraints [PhD thesis]. 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    <title language="eng">Multi-Channel Operation for the Release 2 of ETSI Cooperative Intelligent Transport Systems</title>
    <abstract language="eng">Vehicles and road infrastructure are starting to be equipped with vehicle-to-everything (V2X) communication solutions to increase road safety and provide new services to drivers and passengers. In Europe, the deployment is based on a set of Release 1 standards developed by ETSI to support basic use cases for cooperative intelligent transport systems (C-ITS). For them, the capacity of a single 10 MHz channel in the ITS band at 5.9 GHz is considered sufficient. At the same time, the ITS stakeholders are working towards several advanced use cases, which imply a significant increment of data traffic and the need for multiple channels. To address this issue, ETSI has recently standardized a new multi-channel operation (MCO) concept for flexible, efficient, and future-proof use of multiple channels. This new concept is defined in a set of new specifications that represent the foundation for the future releases of C-ITS standards. The present paper provides a comprehensive review of the new set of specifications, describing the main entities extending the C-ITS architecture at the different layers of the protocol stack, In addition, the paper provides representative examples that describe how these MCO standards will be used in the future and discusses some of the main open issues arising. The review and analysis of this paper facilitate the understanding and motivation of the new set of Release 2 ETSI specifications for MCO and the identification of new research opportunities.</abstract>
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    <title language="eng">Investigation of the Effects Caused by Current Interruption Devices of Lithium Cells at High Overvoltages</title>
    <abstract language="eng">A faulty voltage measurement can lead to the overcharging of a Li-Ion cell, resulting in gas formation and heating inside the cell, which can trigger thermal runaway. To mitigate this risk, cylindrical cells are equipped with a Current Interrupt Device (CID), which functions as a pressure relief valve, disconnecting the electrical circuit within the cell when internal pressure rises. However, this disconnection causes the cell to suddenly become highly resistant, posing a significant issue in series-connected cells. In such configurations, a portion or even the entire system voltage may drop across the disconnected cell, substantially increasing the likelihood of an electric arc. This arc could ignite any escaping flammable gases, leading to catastrophic failures. In a series of tests conducted on three different cell chemistries—NMC (Nickel Manganese Cobalt), NCA (Nickel Cobalt Aluminum), and LFP (Lithium Iron Phosphate)—it was found that the safe operation of the CID cannot be guaranteed for system voltages exceeding 120 V. Although comparative tests at double the nominal cell voltage did not exhibit the same behavior, these findings suggest that current safety standards, which recommend testing at double the nominal voltage, may not adequately address the risks involved. The tests further revealed that series connections of cells with CIDs are inherently dangerous, as, in the worst-case scenario, the entire system voltage can be concentrated across a single cell, leading to potential system failure.</abstract>
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      <last_name>Sevinc</last_name>
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      <first_name>Philipp</first_name>
      <last_name>Lang</last_name>
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      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
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    <title language="eng">Experimental Investigation of Heat Dissipation of Lithium–Ion Cells and Its Correlation with Internal Resistance</title>
    <abstract language="eng">Power loss is a limiting factor for batteries and individual cells. The resulting heat generation due to the power loss leads to reduced battery performance and, thus, lower efficiency. These losses are largely due to the internal resistance of the cells. Therefore, it is important to accurately determine the value of the internal resistance of lithium–ion cells. From the literature, it was found that there are three widely used internal resistance-measurement methods (current step method, direct-energy-loss method, and calorimeter measurement), with negligible research on their comparison demonstrating the most efficient method. Henceforth, to find the most optimal method, this research adopts all three methods on a variety of cell chemistries, including Lithium-ion Manganese Oxide (LMO), Lithium Iron Phosphate (LFP), Nickel Manganese Cobalt (NMC), and Lithium Titanium-Oxide (LTO) for different c-rates (1 C, 2 C, and 3 C), with a wide temperature range (from 0 °C to 40 °C).</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="issn">2076-3417</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-50513</identifier>
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From the literature, it was found that there are three widely used internal resistance-measurement methods (current step method, direct-energy-loss method, and calorimeter measurement), with negligible research on their comparison demonstrating the most efficient method. Henceforth, to find the most optimal method, this research adopts all three methods on a variety of cell chemistries, including Lithium-ion Manganese Oxide (LMO), Lithium Iron Phosphate (LFP), Nickel Manganese Cobalt (NMC), and Lithium Titanium-Oxide (LTO) for different c-rates (1 C, 2 C, and 3 C), with a wide temperature range (from 0 \u00b0C to 40 \u00b0C).&lt;\/jats:p&gt;","DOI":"10.3390\/app14167430","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T08:48:25Z","timestamp":1724662105000},"page":"7430","source":"Crossref","is-referenced-by-count":0,"title":["Experimental Investigation of Heat Dissipation of Lithium\u2013Ion Cells and Its Correlation with Internal Resistance"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"http:\/\/orcid.org\/0009-0000-3838-9910","authenticated-orcid":false,"given":"Stefan Michael","family":"Peringer","sequence":"first","affiliation":[{"name":"CARISSMA Institute for Electric, Connected and Safe Mobility (C-ECOS), Technische Hochschule Ingolstadt, 85049 Ingolstadt, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2730-4904","authenticated-orcid":false,"given":"Yash","family":"Kotak","sequence":"additional","affiliation":[{"name":"CARISSMA Institute for Electric, Connected and Safe Mobility (C-ECOS), Technische Hochschule Ingolstadt, 85049 Ingolstadt, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0184-1322","authenticated-orcid":false,"given":"Hans-Georg","family":"Schweiger","sequence":"additional","affiliation":[{"name":"CARISSMA Institute for Electric, Connected and Safe Mobility (C-ECOS), Technische Hochschule Ingolstadt, 85049 Ingolstadt, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.energy.2012.06.012","article-title":"Effects of electric vehicles on power systems in Northern Europe","volume":"48","author":"Hedegaard","year":"2012","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"372","DOI":"10.3390\/smartcities4010022","article-title":"A Review on Electric Vehicles: Technologies and Challenges","volume":"4","author":"Sanguesa","year":"2021","journal-title":"Smart Cities"},{"key":"ref_3","unstructured":"Jossen, A., and Weydanz, W. 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Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3050","DOI":"10.1016\/j.egyr.2021.05.027","article-title":"Estimation the internal resistance of lithium-ion-battery using a multi-factor dynamic internal resistance model with an error compensation strategy","volume":"7","author":"Chen","year":"2021","journal-title":"Energy Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Barai, A., Uddin, K., Widanage, W.D., McGordon, A., and Jennings, P. (2018). A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells. Sci. Rep., 8.","DOI":"10.1038\/s41598-017-18424-5"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5675","DOI":"10.3390\/en7095675","article-title":"An Acausal Li-Ion Battery Pack Model for Automotive Applications","volume":"7","author":"Uddin","year":"2014","journal-title":"Energies"},{"key":"ref_16","unstructured":"(2011). 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Technical Data Sheet for Dewar Vessels Made of Stainless Steel Vessels of the DSS and GSS Series. Available online: https:\/\/kgw-isotherm.de\/downloads\/datasheet\/dewar\/gss_de.pdf."},{"key":"ref_25","unstructured":"HTR-India (2024, June 07). Wire Wound Resistors Ceramic Encased Type. Available online: https:\/\/htr-india.com\/product\/hea\/."},{"key":"ref_26","unstructured":"Neware (2024, June 07). BTS4000-5V6ABatterytestingsystem. Available online: https:\/\/newarebattery.com\/wp-content\/uploads\/2020\/02\/BTS4000-5V6A-8CH-3Ranges.pdf."},{"key":"ref_27","unstructured":"Julabo (2024, June 07). F32-MA Refrigerated\/Heating Circulator. Available online: https:\/\/julabo.us\/wp-content\/uploads\/products\/datasheets\/JULABO-F32-MA--9153632.pdf."},{"key":"ref_28","unstructured":"Voetsch-Technik (2024, June 07). Temperature Test Chambers VT3 VTS3. 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    <author>
      <first_name>Stefan Michael</first_name>
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      <first_name>Yash</first_name>
      <last_name>Kotak</last_name>
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    <title language="eng">Flexible Operation to Reduce Greenhouse Gas Emissions along the Cold Chain for Chilling, Storage, and Transportation - A Case Study for Dairy Products</title>
    <abstract language="eng">The further expansion of renewable energies in Germany requires flexible consumers to balance fluctuations in electricity production from variable renewable energies. Cold storage warehouses, due to their inherent storage capacity and widespread use, are well-suited for integrating more renewable energies. The potential of cold storage warehouses is often viewed in isolation and not in conjunction with the cold chain’s upstream and downstream processes. By adjusting the temperatures within the processes, the individual links in the cold chain can be made flexible. To assess the effects of flexibilization on emissions and electricity costs, thermodynamic models of the individual links in the cold chain and of a yogurt pallet are developed and linked together. Due to temperature fluctuations in the products resulting from the flexibilization, emission evaluations must be considered throughout the cold chain. Results of the simulation for the study period show that emissions reductions and electricity cost savings can be achieved in all three links when they are made flexible. However, the savings vary in magnitude. Only minor savings can be achieved in the cooling tunnel. The greatest potential for savings is in refrigerated transport, if deeper cooling occurs in the process before, i.e., in the cold storage warehouse.</abstract>
    <parentTitle language="eng">Sustainability</parentTitle>
    <identifier type="issn">2071-1050</identifier>
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    <author>
      <first_name>Martin</first_name>
      <last_name>Stöckl</last_name>
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    <author>
      <first_name>Johannes</first_name>
      <last_name>Idda</last_name>
    </author>
    <author>
      <first_name>Volker</first_name>
      <last_name>Selleneit</last_name>
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    <author>
      <first_name>Uwe</first_name>
      <last_name>Holzhammer</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>flexibility</value>
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      <language>eng</language>
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      <value>flexible energy demand</value>
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    <subject>
      <language>eng</language>
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      <value>system efficiency</value>
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    <subject>
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      <type>uncontrolled</type>
      <value>cold storage warehouse</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>refrigerated transport</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cold chain</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>variable renewable energies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>industry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>dairies</value>
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    <title language="eng">Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors</title>
    <abstract language="eng">With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.</abstract>
    <parentTitle language="eng">Remote Sensing</parentTitle>
    <identifier type="issn">2072-4292</identifier>
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    <author>
      <first_name>Marcel</first_name>
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      <first_name>Nicolas</first_name>
      <last_name>Sarmiento</last_name>
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      <first_name>Hüseyin</first_name>
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      <first_name>Bernhard</first_name>
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      <first_name>Gordon</first_name>
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    <title language="eng">Emergent cooperation from mutual acknowledgment exchange in multi-agent reinforcement learning</title>
    <abstract language="eng">Peer incentivization (PI) is a recent approach where all agents learn to reward or penalize each other in a distributed fashion, which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly incorporated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information, which limits scalability and applicability to real-world scenarios where only local information is accessible. In this paper, we propose Mutual Acknowledgment Token Exchange (MATE), a PI approach defined by a two-phase communication protocol to exchange acknowledgment tokens as incentives to shape individual rewards mutually. All agents condition their token transmissions on the locally estimated quality of their own situations based on environmental rewards and received tokens. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. Our results show that MATE is able to achieve and maintain significantly higher levels of cooperation than previous PI approaches. In addition, we evaluate the robustness of MATE in more realistic scenarios, where agents can deviate from the protocol and communication failures can occur. We also evaluate the sensitivity of MATE w.r.t. the choice of token values.</abstract>
    <parentTitle language="eng">Autonomous Agents and Multi-Agent Systems</parentTitle>
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    <identifier type="issn">1387-2532</identifier>
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Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly incorporated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information, which limits scalability and applicability to real-world scenarios where only local information is accessible. In this paper, we propose &lt;jats:italic&gt;Mutual Acknowledgment Token Exchange (MATE)&lt;\/jats:italic&gt;, a PI approach defined by a two-phase communication protocol to exchange acknowledgment tokens as incentives to shape individual rewards mutually. All agents condition their token transmissions on the locally estimated quality of their own situations based on environmental rewards and received tokens. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. 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    <title language="eng">Deep learning and data augmentation for partial discharge detection in electrical machines</title>
    <abstract language="deu">Fault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair case study. As a result, the two-dimensional convolutional neural network with continuous wavelet transform achieves the best accuracy of around 99.76% on a test dataset of PD signals originating from previously not utilized test objects. All DL models considered in this comparison outperform the state-of-the-art threshold-based PD classification. Even for PD events with an amplitude close to the noise level, the detection rate is still around 95% for the best network. Furthermore, without applying the proposed data augmentation procedure, the DL models investigated are not able to distinguish small PD pulses from noise.</abstract>
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    <title language="eng">Influence of Switching on the Aging of High Energy Lithium-Ion Cells</title>
    <abstract language="eng">An AC-battery or multilevel inverter used to increase safety and flexibility is realizable by switching the cells and modules on and off in a defined way and thus can replace the bidirectional converter. Assessing possible additional aging due to switching, the results of a previous study for a high-power optimized cell showed no influence on the current rates or the switching. In this paper, a highly energy-optimized LG 18650-cell is investigated to discuss the influence of switching during the charge and discharge process, respectively, as well as combining both processes together with clear performance differences when applying higher charge and discharge currents. Moreover, the influence of switching is discussed for the two frequencies (50 Hz and 10 kHz) and different duty cycles. The aging is analyzed by capacity loss and resistance increase, by dV/dQ analysis, and by electrochemical impedance spectroscopy. We found no clear negative influence of switching but a positive effect if the cells are switched during charge. The best performance is found for switching during charge as well as during discharge. The cell aging during switching is clearly determined by the average and not the maximum current applied. This work shows no negative effects of multilevel inverter applications on the tested cells.</abstract>
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    <title language="eng">Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles</title>
    <abstract language="eng">Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.</abstract>
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    <title language="eng">Emergent Cooperation from Mutual Acknowledgment Exchange in Multi-Agent Reinforcement Learning</title>
    <abstract language="eng">Peer incentivization (PI) is a recent approach, where all agents learn to reward or to penalize each other in a distributed fashion which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly integrated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information which limits scalability and applicability to real-world scenarios, where only local information is accessible. In this paper, we propose Mutual Acknowledgment Token Exchange (MATE), a PI approach defined by a two-phase communication protocol to mutually exchange acknowledgment tokens to shape individual rewards. Each agent evaluates the monotonic improvement of its individual situation in order to accept or reject acknowledgment requests from other agents. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. Our results show that MATE is able to achieve and maintain significantly higher levels of cooperation than previous PI approaches. In addition, we evaluate the robustness of MATE in more realistic scenarios, where agents can defect from the protocol and where communication failures can occur. We also evaluate the sensitivity of MATE w.r.t. the choice of token values.</abstract>
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    <title language="eng">Unlocking Past Information: Temporal Embeddings in Cooperative Bird’s Eye View Prediction</title>
    <abstract language="eng">Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2% and under communication failures by up to 19%. The code will be published on GitHub.</abstract>
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    <title language="eng">Hybrid Cu sinter paste for low temperature bonding of bare semiconductors</title>
    <abstract language="deu">A novel hybrid copper paste was developed for low temperature sintering of bare semiconductors. Cu(II) formate (Cu(for)) is complexed in amino-2-propanol (A2P) and added to a paste of etched brass micro flakes. A two-step sintering process is applied: The paste is printed and dried at 120 °C under formic acid (FA) enriched N2 atmosphere (FAN2) for 5 min. Afterwards, bare semiconductors are placed and sintered at 250 °C for 5 min applying a bonding pressure of 20 MPa/10 MPa. By the thermal decomposition of the Cu(for) atomic Cu is released and forms in-situ Cu-nanoparticles. An interconnect is realized with shear strength &gt;100 MPa.</abstract>
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    <title language="eng">Mechanical Properties of Raw Filaments and Printed Specimens: Effects of Fiber Reinforcements and Process Parameters</title>
    <abstract language="eng">Fused Deposition Modeling (FDM) is a well-established manufacturing method for producing both prototype and functional components. This study investigates the mechanical properties of FDM components by material and process-related influencing variables. Tensile tests were conducted on seven different materials in their raw filament form, two of which were fiber-reinforced, to analyze their material-related influence. To cover a wide range from standard to advanced materials relevant for load-carrying components as well as their respective variations, polylactic acid (PLA), 30% wood-fiber-reinforced PLA, acrylonitrile butadiene styrene (ABS), polycarbonate (PC), a blend of ABS and PC, Nylon, and 30% glass-fiber-reinforced Nylon were selected. The process-related influencing variables were studied using the following process parameters: layer thickness, nozzle diameter, build orientation, nozzle temperature, infill density and pattern, and raster angle. The first test series revealed that the addition of wood fibers significantly worsened the mechanical behavior of PLA due to the lack of fiber bonding to the matrix and significant pore formation. The polymer blend of ABS and PC only showed improvements in stiffness. Significant strength and stiffness improvements were found by embedding glass fibers in Nylon, despite partially poor fiber–matrix bonding. The materials with the best properties were selected for the process parameter analysis. When examining the impact of layer thickness on part strength, a clear correlation was evident. Smaller layer thicknesses resulted in higher strength, while stiffness did not appear to be affected. Conversely, larger nozzle diameters and lower nozzle temperatures only positively impacted stiffness, with little effect on strength. The part orientation did alter the fracture behavior of the test specimens. Although an on-edge orientation resulted in higher stiffness, it failed at lower stresses. Higher infill densities and infill patterns aligned with the load direction led to the best mechanical results. The raster angle had a significant impact on the behavior of the printed bodies. An alternating raster angle resulted in lower strengths and stiffness compared to a unidirectional raster angle. However, it also caused significant stretching due to the rotation of the beads.</abstract>
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Env."},{"key":"ref_35","unstructured":"Gibson, L.J., and Ashby, M.F. (2010). Cellular Solids: Structure and Properties, Cambridge University Press. [2nd ed.]. 1. paperback ed. 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    <title language="eng">Influence of Water on Aging Phenomena of Calendric Stored and Cycled Li-Ion Batteries</title>
    <abstract language="eng">We examine the impact of water (160 ± 41 ppm of reference) on the anode, cathode, separator and electrolyte in two aging scenarios: calendric aging (60 °C, 80 days, charged state), resulting in a triggered current interrupt device (CID), and cycling 1680 times (charge/discharge with 1C, 2.75–4.2 V, 20 ± 2 °C), resulting in 24.5% residual capacity. We applied computer tomography (CT), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and secondary ion mass spectrometry (SIMS) to understand the capacity loss. The aged NMC/LCO–graphite cells were compared to a reference cell in pristine state. Both aging scenarios showed (a) thick depositions on the anode and cathode consisting mainly of oxygen, fluorine and phosphorous, (b) reduced separator pore sizes, (c) the deposition of Mn, Co and Ni on top of the anode and (d) the decomposition of the conductive salt LiPF6 accompanied by HF formation and a loss of active lithium. Calendric aging consumes the water content and additionally leads to (e) the decomposition of the organic solvent followed by CO2 gas formation. Cyclic aging increases the water content and additionally results in (f) the consumption of the additive fluoroethylene carbonate (FEC). These findings show how water affects aging phenomena and results in a capacity decrease in the cell.</abstract>
    <parentTitle language="eng">Nanoenergy Advances</parentTitle>
    <identifier type="issn">2673-706X</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-48606</identifier>
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We applied computer tomography (CT), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and secondary ion mass spectrometry (SIMS) to understand the capacity loss. The aged NMC\/LCO\u2013graphite cells were compared to a reference cell in pristine state. Both aging scenarios showed (a) thick depositions on the anode and cathode consisting mainly of oxygen, fluorine and phosphorous, (b) reduced separator pore sizes, (c) the deposition of Mn, Co and Ni on top of the anode and (d) the decomposition of the conductive salt LiPF6 accompanied by HF formation and a loss of active lithium. Calendric aging consumes the water content and additionally leads to (e) the decomposition of the organic solvent followed by CO2 gas formation. Cyclic aging increases the water content and additionally results in (f) the consumption of the additive fluoroethylene carbonate (FEC). These findings show how water affects aging phenomena and results in a capacity decrease in the cell.&lt;\/jats:p&gt;","DOI":"10.3390\/nanoenergyadv4020011","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T15:10:28Z","timestamp":1718982628000},"page":"174-195","source":"Crossref","is-referenced-by-count":0,"title":["Influence of Water on Aging Phenomena of Calendric Stored and Cycled Li-Ion Batteries"],"prefix":"10.3390","volume":"4","author":[{"given":"Gudrun","family":"Wilhelm","sequence":"first","affiliation":[{"name":"Materials Research Institute (IMFAA), Aalen University, Beethovenstr. 1, 73430 Aalen, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6864-1969","authenticated-orcid":false,"given":"Ute","family":"Golla-Schindler","sequence":"additional","affiliation":[{"name":"Materials Research Institute (IMFAA), Aalen University, Beethovenstr. 1, 73430 Aalen, Germany"}]},{"given":"Katharina","family":"W\u00f6hrl","sequence":"additional","affiliation":[{"name":"CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, Germany"}]},{"given":"Christian","family":"Geisbauer","sequence":"additional","affiliation":[{"name":"CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0009-0005-7603-5861","authenticated-orcid":false,"given":"Graham","family":"Cooke","sequence":"additional","affiliation":[{"name":"Hiden Analytical GmbH, Kaiserswerther Stra\u00dfe 215, 40474 D\u00fcsseldorf, Germany"}]},{"given":"Timo","family":"Bernthaler","sequence":"additional","affiliation":[{"name":"Materials Research Institute (IMFAA), Aalen University, Beethovenstr. 1, 73430 Aalen, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0184-1322","authenticated-orcid":false,"given":"Hans-Georg","family":"Schweiger","sequence":"additional","affiliation":[{"name":"CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, Germany"}]},{"given":"Gerhard","family":"Schneider","sequence":"additional","affiliation":[{"name":"Materials Research Institute (IMFAA), Aalen University, Beethovenstr. 1, 73430 Aalen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/S1388-2481(00)00056-4","article-title":"Electrochemical SPM investigation of the solid electrolyte interphase film formed on HOPG electrodes","volume":"2","author":"Alliata","year":"2000","journal-title":"Electrochem. 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    <title language="eng">Data accuracy in Vehicle-to-X cooperative awareness messages: An experimental study for the first commercial deployment of C-ITS in Europe</title>
    <abstract language="eng">Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.</abstract>
    <parentTitle language="eng">Vehicular Communications</parentTitle>
    <identifier type="issn">2214-2096</identifier>
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    <abstract language="eng">It is well known that lithium-ion batteries pose a certain safety risk. The thermal runaway of a cell and the subsequent thermal propagation through the battery are considered particularly dangerous. Effective solutions for their mitigation are therefore of great interest. Previous studies have shown the significant influence&#13;
of a cell’s state of charge (SOC) on its behavior during thermal runaway. This relation may be exploitable in a battery pack to improve its safety. This study aims to assess the leverage of active SOC reduction in the imminent threat of thermal runaway. Implementing such a technique could become feasible with the emergence of reconfigurable battery systems. Four experiments were conducted, each with a module of three fresh 63 Ah high energy pouch cells in a spring-loaded bracing. The experiments studied different stationary SOC configurations, uniform (100% and 60%) and non-uniform (100%–60%–100% and 100%–20%–100%). The results indicate that thermal propagation is substantially delay (87 s) by discharging a cell in its path. The SOC reduction primarily decreases the maximum temperature of the respective cell. Further effects are a calmer thermal runaway and prolonged propagation time within the cell as well as to the next cell. In comparison, the SOC reduction has little impact on the cell’s own triggering time, as the triggering time is mainly determined by the thermal energy transferred from the preceding cell and hence by its SOC. Furthermore, the analysis of the experimental data (temperature, voltage, pressure, video) gives insights into the propagation of thermal runaway through the individual layers of a cell. With reference to the position of a cell relative to the origin of the thermal propagation, a decrease of its mass loss and an increase of its internal propagation time is observed. This effect is attributed to the decreasing module pressure due to progressive loss of material. The assessment shows that active SOC reduction techniques have great leverage for mitigating or even stopping thermal propagation in a battery pack.</abstract>
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