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

被引:1
|
作者
Long, Teng [1 ]
Wang, Leyu [2 ]
Kan, Cing-Dao [2 ]
机构
[1] Univ Cincinnati, Coll Engn & Appl Sci, Dept Mech & Mat Engn, 2901 Woodside Dr, Cincinnati, OH 45221 USA
[2] George Mason Univ, Coll Sci, Ctr Collis Safety & Anal, 4400 Univ Dr, Fairfax, VA 22030 USA
来源
ENGINEERING RESEARCH EXPRESS | 2024年 / 6卷 / 03期
关键词
electric vehicles; battery cell safety; experiment; material model; simulation framework; machine learning; LITHIUM-ION BATTERY; ELECTROCHEMICAL-THERMAL MODEL; SHORT-CIRCUIT; MECHANICAL INTEGRITY; LOW-TEMPERATURE; CELL; POUCH; STATE; SIMULATION; SEPARATOR;
D O I
10.1088/2631-8695/ad734d
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
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页数:20
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