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Rapid prediction method for thermal runaway propagation in battery pack based on lumped thermal resistance network and electric circuit analogy

Author

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  • Jiang, Z.Y.
  • Qu, Z.G.
  • Zhang, J.F.
  • Rao, Z.H.

Abstract

High-power lithium-ion batteries (LIBs) suffer from thermal runaway (TR) under unusual forces and misuse. Consequent TR propagation can cause battery pack breakdown and even dangerous fires or explosions. In this study, a four-step TR propagation prediction method is proposed for large-scale battery packs with series and parallel connection. To investigate the TR propagation behavior in battery packs, a lumped thermal resistance network was constructed based on the heat transfer characteristics of LIB packs. The energy balance equation for each cell and heat exchange between cells were solved via electrical circuit analogy. TR propagation features are discussed with different TR trigger locations, and the TR prevention effect of phase change materials (PCMs) is evaluated. The proposed prediction method exhibits high computational efficiency and adequate accuracy in resolving TR propagation. The prediction of battery core temperature and the experimental results are good in agreement. The TR propagation includes three stages: initial stage, rapid expanding stage and burst stage. The TR propagates preferentially along the thermal path with lower thermal resistance. When a PCM is applied to prevent TR, the critical behavior of TR propagation prevention is discovered. If the trigger cell of TR is prevented with the PCM, TR propagation in the entire pack can be avoided. The proposed method fulfils the fast prediction of TR propagation, which can provide insights into the on-board thermal safety design of electric vehicles.

Suggested Citation

  • Jiang, Z.Y. & Qu, Z.G. & Zhang, J.F. & Rao, Z.H., 2020. "Rapid prediction method for thermal runaway propagation in battery pack based on lumped thermal resistance network and electric circuit analogy," Applied Energy, Elsevier, vol. 268(C).
  • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920305195
    DOI: 10.1016/j.apenergy.2020.115007
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    References listed on IDEAS

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    3. Yu, Hanqing & Zhang, Lisheng & Wang, Wentao & Li, Shen & Chen, Siyan & Yang, Shichun & Li, Junfu & Liu, Xinhua, 2023. "State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries," Energy, Elsevier, vol. 278(C).
    4. Ouyang, Nan & Zhang, Wencan & Yin, Xiuxing & Li, Xingyao & Xie, Yi & He, Hancheng & Long, Zhuoru, 2023. "A data-driven method for predicting thermal runaway propagation of battery modules considering uncertain conditions," Energy, Elsevier, vol. 273(C).
    5. Chen, Jie & Ren, Dongsheng & Hsu, Hungjen & Wang, Li & He, Xiangming & Zhang, Caiping & Feng, Xuning & Ouyang, Minggao, 2021. "Investigating the thermal runaway features of lithium-ion batteries using a thermal resistance network model," Applied Energy, Elsevier, vol. 295(C).
    6. Liang, Lin & Zhao, Yaohua & Diao, Yanhua & Ren, Ruyang & Jing, Heran, 2021. "Inclined U-shaped flat microheat pipe array configuration for cooling and heating lithium-ion battery modules in electric vehicles," Energy, Elsevier, vol. 235(C).
    7. Wang, Gongquan & Kong, Depeng & Ping, Ping & He, Xiaoqin & Lv, Hongpeng & Zhao, Hengle & Hong, Wanru, 2023. "Modeling venting behavior of lithium-ion batteries during thermal runaway propagation by coupling CFD and thermal resistance network," Applied Energy, Elsevier, vol. 334(C).
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