Experimental and modeling approaches for electric vehicle battery safety: a technical review

Driven by the rising number of fire incidents involving Battery Electric Vehicles (BEVs), this work reviews the current state of knowledge in electric vehicle battery safety, focusing on simulation and experiment methodologies. The critical importance of battery safety is emphasized by the potential...

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Veröffentlicht in:Engineering Research Express 2024-09, Vol.6 (3), p.32503
Hauptverfasser: Long, Teng, Wang, Leyu, Kan, Cing-Dao
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Sprache:eng
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Zusammenfassung:Driven by the rising number of fire incidents involving Battery Electric Vehicles (BEVs), this work reviews the current state of knowledge in electric vehicle battery safety, focusing on simulation and experiment methodologies. The critical importance of battery safety is emphasized by the potential for thermal runaway and fires due to various factors. These factors include design and manufacturing flaws, excessive current loads, mechanical damage, improper charging practices (overcharging/overdischarging), extreme temperature exposure, and even as-yet unidentified causes. This study provides a comprehensive review of methodologies employed in lithium-ion battery safety modeling and experiment for BEVs. The review includes various aspects. It includes the high voltage battery system in BEVs, battery safety considerations in BEVs, geometry modeling of battery cells, material modeling of battery cells, simulation framework for batteries, cell-level experiment, testing of materials for cell components, and the application of machine learning. Physics-based simulations that accurately predict battery thermal runaway are crucial for guaranteeing the safety and optimizing the performance of BEVs. While Finite Element Analysis (FEA) is a well-established technique for evaluating the crashworthiness of conventional vehicles, its application to BEVs presents several significant challenges. However, limited literature exists on cell-level experiments involving spray and dropping scenarios. Furthermore, additional data on melting points, thermal properties, and porosity is necessary for component-level testing. This work also highlights the need for robust friction and fatigue models, which remain a critical knowledge gap in this field. Finally, the integration of machine learning approaches for constitutive laws and the development of more complex frameworks are essential advancements for future research. This review is expected to provide a guide in simulation and experiment in EV battery safety engineering.
ISSN:2631-8695
2631-8695
DOI:10.1088/2631-8695/ad734d