<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <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>1453</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue>39</issue>
    <volume>2021</volume>
    <articleNumber>102588</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Real-time core temperature prediction of prismatic automotive lithium-ion battery cells based on artificial neural networks</title>
    <parentTitle language="eng">Journal of Energy Storage</parentTitle>
    <identifier type="issn">2352-1538</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.est.2021.102588</enrichment>
    <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>Battery modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Electro-thermal model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermal model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Real-time application</value>
    </subject>
    <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>
  </doc>
</export-example>
