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A safety performance estimation model of lithium-ion batteries for electric vehicles under dynamic compression

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  • Yiding, Li
  • Wenwei, Wang
  • Cheng, Lin
  • Xiaoguang, Yang
  • Fenghao, Zuo

Abstract

Dynamic compression is a common scenario of mechanical abuse of lithium-ion batteries for electric vehicles. The safety characteristics under dynamic compression is highly different from that under quasi-static compression, whereas research in this field is still scarce. Here, the lateral quasi-static compression and dynamic compression of two kinds of cylindrical lithium-ion batteries are carried out. According to the first order derivative of the force to the displacement, there are six main stages of the battery behavior under lateral compression. The behavior of STAGE IV under the dynamic compression is obviously different from that under quasi-static compression. Both experimental and modeling results reveal that the dynamic load has a certain strengthening effect on the battery. For both types of batteries, the equivalent strength under high strain rate tends to be consistent. Combined with the Crushable-Foam material model and Johnson-Cook material model, the dynamic and quasi-static mechanical simulation are performed at the cell level, and the simulation results well explain the experimental phenomenon. The simulation and experimental results show that the safety warning of the cylindrical lithium-ion battery based on mechanical penetration has a certain safety margin, which can provide valuable reference for the battery safety under mechanical abuse in the future.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pa:s0360544220321575
    DOI: 10.1016/j.energy.2020.119050
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    References listed on IDEAS

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    Cited by:

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    2. Jiang, Lulu & Deng, Zhongwei & Tang, Xiaolin & Hu, Lin & Lin, Xianke & Hu, Xiaosong, 2021. "Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data," Energy, Elsevier, vol. 234(C).
    3. Xu, Dongxin & Pan, Yongjun & Zhang, Xiaoxi & Dai, Wei & Liu, Binghe & Shuai, Qi, 2024. "Data-driven modelling and evaluation of a battery-pack system’s mechanical safety against bottom cone impact," Energy, Elsevier, vol. 290(C).
    4. Li, Honggang & Zhou, Dian & Zhang, Meihe & Liu, Binghe & Zhang, Chao, 2023. "Multi-field interpretation of internal short circuit and thermal runaway behavior for lithium-ion batteries under mechanical abuse," Energy, Elsevier, vol. 263(PE).
    5. Borge-Diez, David & Icaza, Daniel & Açıkkalp, Emin & Amaris, Hortensia, 2021. "Combined vehicle to building (V2B) and vehicle to home (V2H) strategy to increase electric vehicle market share," Energy, Elsevier, vol. 237(C).

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