TY - JOUR A1 - Kotak, Yash A1 - Marchante Fernández, Carlos A1 - Canals Casals, Lluc A1 - Kotak, Bhavya A1 - Koch, Daniel A1 - Geisbauer, Christian A1 - Trilla, Lluís A1 - Gómez-Nuñez, Alberto A1 - Schweiger, Hans-Georg T1 - End of Electric Vehicle Batteries BT - Reuse vs. Recycle JF - Energies N2 - 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. UR - https://doi.org/10.3390/en14082217 KW - battery recycling KW - battery reuse KW - battery second life KW - circular economy KW - lithium-ion cells KW - electric vehicles KW - battery components recycling KW - sustainability in mobility KW - battery safety KW - battery cost analysis Y1 - 2021 UR - https://doi.org/10.3390/en14082217 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10115 SN - 1996-1073 VL - 14 IS - 8 PB - MDPI CY - Basel ER - TY - JOUR A1 - Wöhrl, Katharina A1 - Geisbauer, Christian A1 - Nebl, Christoph A1 - Lott, Susanne A1 - Schweiger, Hans-Georg T1 - Crashed Electric Vehicle Handling and Recommendations - State of the Art in Germany JF - Energies N2 - 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. UR - https://doi.org/10.3390/en14041040 KW - electric vehicle KW - battery KW - high voltage KW - accident KW - recommendation KW - energy storage KW - thermal runaway KW - fire extinguishing KW - rescue service KW - handling Y1 - 2021 UR - https://doi.org/10.3390/en14041040 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10123 SN - 1996-1073 VL - 14 IS - 4 PB - MDPI CY - Basel ER - TY - INPR A1 - Song, Rui A1 - Liu, Dai A1 - Chen, Dave Zhenyu A1 - Festag, Andreas A1 - Trinitis, Carsten A1 - Schulz, Martin A1 - Knoll, Alois T1 - Federated Learning via Decentralized Dataset Distillation in Resource Constrained Edge Environments N2 - 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. UR - https://doi.org/10.48550/arXiv.2208.11311 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2208.11311 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60054 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Knierim, Pascal A1 - Prange, Sarah A1 - Alt, Florian A1 - Feger, Sebastian A1 - Schneegass, Stefan A1 - Sasse, M. Angela A1 - Bayerl, Dominik A1 - Hof, Hans-Joachim ED - Marky, Karola ED - Grünefeld, Uwe ED - Kosch, Thomas T1 - Inclusive Security by Design T2 - Mensch und Computer 2022 - Workshopband UR - https://doi.org/10.18420/muc2022-mci-ws14-128 KW - usable security KW - inclusion KW - self-determined security Y1 - 2022 UR - https://doi.org/10.18420/muc2022-mci-ws14-128 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Bayerl, Dominik A1 - Hutzelmann, Thomas A1 - Hof, Hans-Joachim T1 - Efficient Cross-Architecture Binary Function Embeddings through Knowledge Distillation T2 - Proceedings of the 11th ACM Cyber-Physical System Security Workshop N2 - Deep learning has recently been shown to be effective in various tasks related to static binary analysis. One important analysis task is the binary function similarity problem: Given the binary code of two functions compiled with different compilers, different settings, and different processor architectures, the goal is to decide whether the functions are semantically equivalent (i.e. "similar") or not. This problem has numerous applications for embedded systems, for example plagiarism detection, validation of compliance restrictions with usable software licenses, more efficient reverse engineering of existing binary codebases, or vulnerability scanning by detecting known vulnerable functions. In this paper, we propose a novel training scheme for the popular transformer neural network architecture to learn function embeddings directly from instruction listings. Unlike existing approaches, our solution explicitly considers the cross-architecture scenario: we propose a training method to adapt the model to different instruction set architectures (ISA) without having to train a new model from scratch, which allows the model to also be used efficiently for embedded systems, where there are a variety of different processor architectures. We show that our solution achieves a similarity classification accuracy of 89.6% on a dataset consisting of several real-world open source software projects. Finally, we conduct extensive experiments to demonstrate the effectiveness of knowledge distillation in increasing the computational efficiency of the embedding model. We demonstrate a reduction in the number of parameters from 87M to 23M, while still maintaining a classification accuracy of 87.8%. Our code and artifacts are available as open source. UR - https://doi.org/10.1145/3709017.3737709 Y1 - 2025 UR - https://doi.org/10.1145/3709017.3737709 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61584 SN - 979-8-4007-1413-9 SP - 43 EP - 51 PB - ACM CY - New York ER - TY - INPR A1 - Song, Rui A1 - Liang, Chenwei A1 - Xia, Yan A1 - Zimmer, Walter A1 - Cao, Hu A1 - Caesar, Holger A1 - Festag, Andreas A1 - Knoll, Alois T1 - CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving N2 - 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. UR - https://doi.org/10.48550/arXiv.2503.06744 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2503.06744 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58313 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Heinl, Patrizia A1 - Patapovas, Andrius A1 - Pilgermann, Michael T1 - Towards AI-enabled Cyber Threat Assessment in the Health Sector N2 - Cyber attacks on the healthcare industry can have tremendous consequences and the attack surface expands continuously. In order to handle the steadily rising workload, an expanding amount of analog processes in healthcare institutions is digitized. Despite regulations becoming stricter, not all existing infrastructure is sufficiently protected against cyber attacks. With an increasing number of devices and digital processes, the system and network landscape becomes more complex and harder to manage and therefore also more difficult to protect. The aim of this project is to introduce an AI-enabled platform that collects security relevant information from the outside of a health organization, analyzes it, delivers a risk score and supports decision makers in healthcare institutions to optimize investment choices for security measures. Therefore, an architecture of such a platform is designed, relevant information sources are identified, and AI methods for relevant data collection, selection, and risk scoring are explored. UR - https://doi.org/10.48550/arXiv.2409.12765 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2409.12765 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58381 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Barbosa da Silva, Leonardo A1 - Lobo, Silas A1 - Fernández, Evelio A1 - Facchi, Christian ED - Vinel, Alexey ED - Berns, Karsten ED - Ploeg, Jeroen ED - Gusikhin, Oleg T1 - What Is the Right Bounding Box of a VRU Cluster in V2X Communication? How to Form a Good Shape? T2 - Vehits 2024: 10th International Conference on Vehicle Technology and Intelligent Transport Systems Proceedings N2 - Among the possible traffic members on a Vehicle-to-Everything network, the term Vulnerable Road User (VRU) is assigned e.g. to pedestrians and cyclists. The VRU Awareness Message (VAM) is used by VRUs to inform other users of their presence and ensure they are perceived in a traffic system. Since the number of VRUs in crowded areas might be very high, the over-the-air traffic might be overloaded. To reduce channel overload, VAMs offer a clustering feature in which VRUs with similar kinematics and positions can group themselves so that only one device transmits messages. The VRU Basic Service specification describes the cluster as a bounding box that must cover all its members using a geometric shape so that other vehicles in the vicinity can avoid colliding with the contained VRUs. This paper contributes to the standardization effort by introducing a data structure, the Cluster Map, for the clustering in the VRU Basic Service. Furthermore, this work is the first to suggest strategies for forming bounding box shapes. Simulation results show that each of the geometry types is useful in different situations, thus further research on the topic is advised. UR - https://doi.org/10.5220/0012699100003702 Y1 - 2024 UR - https://doi.org/10.5220/0012699100003702 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48553 SN - 978-989-758-703-0 SP - 144 EP - 155 PB - SciTePress CY - Setúbal ER - TY - INPR A1 - Paula, Daniel A1 - Bauder, Maximilian A1 - Pfeilschifter, Claus A1 - Petermeier, Franziska A1 - Kubjatko, Tibor A1 - Böhm, Klaus A1 - Riener, Andreas A1 - Schweiger, Hans-Georg T1 - 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 N2 - 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. UR - https://doi.org/10.20944/preprints202311.0947.v1 Y1 - 2023 UR - https://doi.org/10.20944/preprints202311.0947.v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42173 PB - Preprints CY - Basel ER - TY - CHAP A1 - Eder, Lukas A1 - Löw, Jakob A1 - Hof, Hans-Joachim T1 - Charging Communication Sniffing and Man-in-the-Middle Attacks T2 - Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems N2 - In recent years an increasing amount of electric vehicle fast charging stations have been built to meet the growing demand from rising electric vehicle numbers. The standard for fast charging communication in europe is ISO 15118. In theory the standard includes security controls for authentication and transport encryption. In reality difficulties with implementing those security controls as well as insecure design within the standard lead to multiple possible attack vectors compromising the confidentiality and authenticity of charging communication sessions. The goal of this research is to present different approaches towards performing sniffing and man-in-the-middle attacks against charging communication. We also provide a novel approach, which does not rely on race conditions and thus is more reliable than previous approaches. UR - ttps://doi.org/10.1145/3679240.3734648 Y1 - 2025 UR - ttps://doi.org/10.1145/3679240.3734648 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60183 SN - 979-8-4007-1125-1 SP - 799 EP - 804 PB - ACM CY - New York ER - TY - CHAP A1 - Song, Rui A1 - Festag, Andreas A1 - Jagtap, Abhishek Dinkar A1 - Bialdyga, Maximilian A1 - Yan, Zhiran A1 - Otte, Maximilian A1 - Sadashivaiah, Sanath Tiptur A1 - Knoll, Alois T1 - First Mile: An Open Innovation Lab for Infrastructure-Assisted Cooperative Intelligent Transportation Systems T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588500 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588500 SN - 979-8-3503-4881-1 SP - 1635 EP - 1642 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Hegde, Anupama A1 - Lobo, Silas A1 - Festag, Andreas T1 - Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS T2 - 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) UR - https://doi.org/10.1109/WiMob55322.2022.9941707 KW - Radio resource allocation in Cellular-V2X KW - VRU safety and protection KW - messaging services Y1 - 2022 UR - https://doi.org/10.1109/WiMob55322.2022.9941707 SN - 978-1-6654-6975-3 SP - 118 EP - 123 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Song, Rui A1 - Liu, Dai A1 - Chen, Dave Zhenyu A1 - Festag, Andreas A1 - Trinitis, Carsten A1 - Schulz, Martin A1 - Knoll, Alois T1 - Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments T2 - IJCNN 2023 Conference Proceedings UR - https://doi.org/10.1109/IJCNN54540.2023.10191879 Y1 - 2023 UR - https://doi.org/10.1109/IJCNN54540.2023.10191879 SN - 978-1-6654-8867-9 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Lobo, Silas A1 - Festag, Andreas A1 - Facchi, Christian T1 - Enhancing the Safety of Vulnerable Road Users: Messaging Protocols for V2X Communication T2 - 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Proceedings UR - https://doi.org/10.1109/VTC2022-Fall57202.2022.10012775 KW - road safety KW - vulnerable road users KW - V2X communication KW - messaging KW - collective perception KW - CPM KW - VAM KW - VRU Y1 - 2023 UR - https://doi.org/10.1109/VTC2022-Fall57202.2022.10012775 SN - 978-1-6654-5468-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mayer, Kevin A1 - Volkersdorfer, Tina A1 - Hofbauer, Jenny A1 - Heinl, Patrizia A1 - Hof, Hans-Joachim ED - Hussain, Fatima ED - Fries, Steffen T1 - Vehicle Security Operations Center for Cooperative, Connected and Automated Mobility T2 - SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_190_30080.html SN - 978-1-68558-206-7 SP - 156 EP - 164 PB - IARIA CY - [s. l.] ER - TY - CHAP A1 - Michl, Marco A1 - Hof, Hans-Joachim ED - Hussain, Fatima ED - Fries, Steffen T1 - Towards a Stakeholder-Centric Trust Management Approach for the Automotive Ecosystem T2 - SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_70_30036.html SN - 978-1-68558-206-7 SP - 64 EP - 70 PB - IARIA CY - [s. l.] ER - TY - CHAP A1 - Löw, Jakob A1 - Mayer, Kevin A1 - Hof, Hans-Joachim ED - Hussain, Fatima ED - Fries, Steffen T1 - Fast Charging Communication and Cybersecurity: A Technology Review T2 - SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_50_30030.html SN - 978-1-68558-206-7 SP - 52 EP - 57 PB - IARIA CY - [s. l.] ER - TY - CHAP A1 - Hofbauer, Jenny A1 - Mayer, Kevin ED - Hussain, Fatima ED - Fries, Steffen T1 - Blue Team Fundamentals: Roles and Tools in a Security Operations Center T2 - SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_220_30089.html SN - 978-1-68558-206-7 SP - 176 EP - 184 PB - IARIA CY - [s. l.] ER - TY - INPR A1 - Morris, John A1 - Tatschner, Stefan A1 - Heinl, Michael P. A1 - Heinl, Patrizia A1 - Newe, Thomas A1 - Plaga, Sven T1 - Cybersecurity as a Service UR - https://doi.org/10.48550/arXiv.2402.13965 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2402.13965 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics JF - Sensors N2 - 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. UR - https://doi.org/10.3390/s23010159 KW - On-Board Diagnostics KW - Machine Learning KW - Transformer Neural Network KW - Autonomous Driving KW - ADAS KW - Inertial Navigation Systems Y1 - 2022 UR - https://doi.org/10.3390/s23010159 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30464 SN - 1424-8220 VL - 23 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Delooz, Quentin A1 - Willecke, Alexander A1 - Garlichs, Keno A1 - Hagau, Andreas-Christian A1 - Wolf, Lars A1 - Vinel, Alexey A1 - Festag, Andreas T1 - Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2022.3170029 KW - Collective perception KW - information redundancy mitigation KW - road safety KW - sensor data sharing KW - V2X communications Y1 - 2022 UR - https://doi.org/10.1109/ACCESS.2022.3170029 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28662 SN - 2169-3536 VL - 10 SP - 47076 EP - 47093 PB - IEEE CY - New York ER - TY - CHAP A1 - Meilinger, Florian A1 - Haß, Jan A1 - Schieber, Christina A1 - Schweiger, Hans-Georg ED - Vaculin, Ondrej T1 - Investigation of the hazard potential of lithium-ion cells in case of overcharging with automotive oriented high-voltage levels T2 - Applied Research Conference 2023 Conference Proceedings: 3rd July 2023 Technische Hochschule Ingolstadt UR - https://doi.org/10.57825/repo_in-5244 Y1 - 2023 UR - https://doi.org/10.57825/repo_in-5244 SP - 254 EP - 259 PB - Technische Hochschule Ingolstadt CY - Ingolstadt ER - TY - CHAP A1 - Walter, Gero A1 - Schweiger, Hans-Georg ED - Vaculin, Ondrej T1 - Structural Health Monitoring of Hydrogen Pressure Vessels for Fuel Cell Electric Vehicles T2 - Applied Research Conference 2023 Conference Proceedings: 3rd July 2023 Technische Hochschule Ingolstadt UR - https://doi.org/10.57825/repo_in-5244 Y1 - 2023 UR - https://doi.org/10.57825/repo_in-5244 SP - 282 EP - 287 PB - Technische Hochschule Ingolstadt CY - Ingolstadt ER -