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Safety modelling and testing of lithium-ion batteries in electrified vehicles

Author

Listed:
  • Jie Deng

    (Ford Motor Company)

  • Chulheung Bae

    (Ford Motor Company)

  • James Marcicki

    (Ford Motor Company)

  • Alvaro Masias

    (Ford Motor Company)

  • Theodore Miller

    (Ford Motor Company)

Abstract

To optimize the safety of batteries, it is important to understand their behaviours when subjected to abuse conditions. Most early efforts in battery safety modelling focused on either one battery cell or a single field of interest such as mechanical or thermal failure. These efforts may not completely reflect the failure of batteries in automotive applications, where various physical processes can take place in a large number of cells simultaneously. In this Perspective, we review modelling and testing approaches for battery safety under abuse conditions. We then propose a general framework for large-scale multi-physics modelling and experimental work to address safety issues of automotive batteries in real-world applications. In particular, we consider modelling coupled mechanical, electrical, electrochemical and thermal behaviours of batteries, and explore strategies to extend simulations to the battery module and pack level. Moreover, we evaluate safety test approaches for an entire range of automotive hardware sets from cell to pack. We also discuss challenges in building this framework and directions for its future development.

Suggested Citation

  • Jie Deng & Chulheung Bae & James Marcicki & Alvaro Masias & Theodore Miller, 2018. "Safety modelling and testing of lithium-ion batteries in electrified vehicles," Nature Energy, Nature, vol. 3(4), pages 261-266, April.
    • Handle: RePEc:nat:natene:v:3:y:2018:i:4:d:10.1038_s41560-018-0122-3
    DOI: 10.1038/s41560-018-0122-3
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    Citations

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    Cited by:

    1. Yiding, Li & Wenwei, Wang & Cheng, Lin & Xiaoguang, Yang & Fenghao, Zuo, 2021. "A safety performance estimation model of lithium-ion batteries for electric vehicles under dynamic compression," Energy, Elsevier, vol. 215(PA).
    2. Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
    3. Feng Zhu & Runzhou Zhou & David J. Sypeck, 2020. "Numerical Modeling and Safety Design for Lithium-Ion Vehicle Battery Modules Subject to Crush Loading," Energies, MDPI, vol. 14(1), pages 1-24, December.
    4. Changhong Jiang & Qiming Wang & Niaona Zhang & Haitao Ding, 2022. "Overcurrent Protection and Unmatched Disturbance Rejection under Non-Cascade Structure for PMSM," Energies, MDPI, vol. 15(18), pages 1-11, September.
    5. Genwei Wang & Xuanfu Guo & Jingyi Chen & Pengfei Han & Qiliang Su & Meiqing Guo & Bin Wang & Hui Song, 2023. "Safety Performance and Failure Criteria of Lithium-Ion Batteries under Mechanical Abuse," Energies, MDPI, vol. 16(17), pages 1-25, September.
    6. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
    7. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    8. Xia, Quan & Ren, Yi & Wang, Zili & Yang, Dezhen & Yan, Peiyu & Wu, Zeyu & Sun, Bo & Feng, Qiang & Qian, Cheng, 2023. "Safety risk assessment method for thermal abuse of lithium-ion battery pack based on multiphysics simulation and improved bisection method," Energy, Elsevier, vol. 264(C).
    9. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    10. Jingzhao Zhang & Yanan Wang & Benben Jiang & Haowei He & Shaobo Huang & Chen Wang & Yang Zhang & Xuebing Han & Dongxu Guo & Guannan He & Minggao Ouyang, 2023. "Realistic fault detection of li-ion battery via dynamical deep learning," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    11. Hongrui Liu & Xiangyang Wei & Junjie Ai & Xudong Yang, 2024. "A Layered Parallel Equaliser Based on Flyback Transformer Multiplexed for Lithium-Ion Battery System," Energies, MDPI, vol. 17(3), pages 1-15, February.
    12. Zhang, Wencan & Li, Xingyao & Liu, Guote & Ouyang, Nan & Yuan, Jiangfeng & Xie, Yi & Wu, Weixiong, 2024. "Optimization design of a hybrid thermal runaway propagation mitigation system for power battery module using high-dimensional surrogate models," Renewable Energy, Elsevier, vol. 225(C).

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