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

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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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    Cited by:

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    10. Daniels, Rojo Kurian & Kumar, Vikas & Chouhan, Satyendra Singh & Prabhakar, Aneesh, 2024. "Thermal runaway fault prediction in air-cooled lithium-ion battery modules using machine learning through temperature sensors placement optimization," Applied Energy, Elsevier, vol. 355(C).
    11. Chen, Mingyi & Yu, Yue & Ouyang, Dongxu & Weng, Jingwen & Zhao, Luyao & Wang, Jian & Chen, Yin, 2024. "Research progress of enhancing battery safety with phase change materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    12. Ting Quan & Qi Xia & Xiaoyu Wei & Yanli Zhu, 2024. "Recent Development of Thermal Insulating Materials for Li-Ion Batteries," Energies, MDPI, vol. 17(17), pages 1-37, September.
    13. Deng, Jian & Huang, Qiqiu & Li, Xinxi & Zhang, Guoqing & Li, Canbing & Li, Songbo, 2024. "Influence mechanism of battery thermal management with flexible flame retardant composite phase change materials by temperature aging," Renewable Energy, Elsevier, vol. 222(C).
    14. 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).
    15. 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).

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