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Coupling effect of state of charge and loading rate on internal short circuit of lithium-ion batteries induced by mechanical abuse

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  • Wang, Can
  • Wang, Renjie
  • Zhang, Chengming
  • Yu, Quanqing

Abstract

Mechanical safety of lithium-ion batteries (LIBs) is the key factor restricting the development of electric vehicles. A critical assessment of their mechanical safety involves the evaluation of mechanical-electrical-thermal characteristics of lithium-ion batteries during internal short circuits (ISCs) induced by mechanical abuse. This study comprehensively analyzes these characteristics under the coupling influence of state of charge (SOC) and loading rate. The findings reveal the “densification→fracture→secondary densification→secondary fracture” process of the battery at 1 mm/min loading rate. The separator assumes a pivotal role in shaping the fracture failure characteristics of the battery components. Besides, within conventional SOC, the ISC duration displacement increases with SOC increasing at 1 mm/min loading rate, while the slightly overcharged LIBs trigger thermal runaway rapidly. The results also present that coupled SOC and loading rate effects are present in the mechanical and electrical characteristics, while absence in the thermal characteristics. The battery performs the SOC dependence at 500 mm/min loading rate, but not at 1 mm/min and 60 mm/min loading rate. The variation of ISC duration time with SOC also differs at 500 mm/min loading rate compared to others. While the association between maximum temperature and SOC at different loading rates perform less variability. The insights derived from this study could contribute valuable theoretical guidance for the mechanical simulation of the battery and the design of the mechanically safe battery pack.

Suggested Citation

  • Wang, Can & Wang, Renjie & Zhang, Chengming & Yu, Quanqing, 2024. "Coupling effect of state of charge and loading rate on internal short circuit of lithium-ion batteries induced by mechanical abuse," Applied Energy, Elsevier, vol. 375(C).
  • Handle: RePEc:eee:appene:v:375:y:2024:i:c:s0306261924015216
    DOI: 10.1016/j.apenergy.2024.124138
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    References listed on IDEAS

    as
    1. Yu, Quanqing & Nie, Yuwei & Guo, Shanshan & Li, Junfu & Zhang, Chengming, 2024. "Machine learning enables rapid state of health estimation of each cell within battery pack," Applied Energy, Elsevier, vol. 375(C).
    2. Wang, Lubing & Li, Jianping & Chen, Jiaying & Duan, Xudong & Li, Binqi & Li, Jiani, 2023. "Revealing the internal short circuit mechanisms in lithium-ion batteries upon dynamic loading based on multiphysics simulation," Applied Energy, Elsevier, vol. 351(C).
    3. Hong, Jichao & Wang, Zhenpo & Qu, Changhui & Zhou, Yangjie & Shan, Tongxin & Zhang, Jinghan & Hou, Yankai, 2022. "Investigation on overcharge-caused thermal runaway of lithium-ion batteries in real-world electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    4. Jia, Yikai & Yin, Sha & Liu, Binghe & Zhao, Hui & Yu, Huili & Li, Jie & Xu, Jun, 2019. "Unlocking the coupling mechanical-electrochemical behavior of lithium-ion battery upon dynamic mechanical loading," Energy, Elsevier, vol. 166(C), pages 951-960.
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    1. Yu, Quanqing & Nie, Yuwei & Guo, Shanshan & Li, Junfu & Zhang, Chengming, 2024. "Machine learning enables rapid state of health estimation of each cell within battery pack," Applied Energy, Elsevier, vol. 375(C).

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