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Safety risk assessment method for thermal abuse of lithium-ion battery pack based on multiphysics simulation and improved bisection method

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Listed:
  • Xia, Quan
  • Ren, Yi
  • Wang, Zili
  • Yang, Dezhen
  • Yan, Peiyu
  • Wu, Zeyu
  • Sun, Bo
  • Feng, Qiang
  • Qian, Cheng

Abstract

The thermal safety of lithium-ion (Li-ion) batteries is of great importance for their further development and application. The accurate evaluation of the thermal safety boundary has always been the key issue. Due to the coupling effect of the internal, external, and randomness factors, the risk probability is difficult to evaluate using existing deterministic analyses. Therefore, a safety risk assessment method for the thermal abuse of Li-ion battery packs is proposed, and an improved bisection-method-based analysis algorithm for the thermal safety boundary is established. Moreover, a multiphysics model is developed considering an equivalent circuit, thermal abuse, and a fluid dynamics model. Furthermore, stochastic models of the battery parameters and loading are constructed to describe the randomness. The temperature and power stress–strength interference models are integrated to evaluate the thermal safety risk of the battery pack. Then, the models are validated by the temperature and analyzing the stochastic parameters. Finally, several case studies are implemented, including the analyses of thermal safety boundary, effect of stochastic parameters, safety risk probability, and thermal runaway propagation in different scenarios. The results denote that the effect of internal resistance dispersion is dominant and that the risk probability will increase with degradation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222031140
    DOI: 10.1016/j.energy.2022.126228
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    References listed on IDEAS

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    Cited by:

    1. Dong, Chenchen & Sun, Dashuai, 2024. "Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
    2. Nikolaos Fesakis & Georgios Falekas & Ilias Palaiologou & Georgia Eirini Lazaridou & Athanasios Karlis, 2024. "Integration and Optimization of Multisource Electric Vehicles: A Critical Review of Hybrid Energy Systems, Topologies, and Control Algorithms," Energies, MDPI, vol. 17(17), pages 1-42, August.

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