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An integrated methodology for dynamic risk prediction of thermal runaway in lithium-ion batteries

Author

Listed:
  • Huixing Meng

    (BIT - Beijing Institute of Technology)

  • Qiaoqiao Yang

    (BIT - Beijing Institute of Technology)

  • Enrico Zio

    (CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres, POLIMI - Politecnico di Milano [Milan])

  • Jinduo Xing

    (BIT - Beijing Institute of Technology)

Abstract

The risk of thermal runaway in lithium-ion battery (LIB) attracts significant attention from domains of society, industry, and academia. However, the thermal runaway prediction in the framework of system safety requires further efforts. In this paper, we propose a methodology for dynamic risk prediction by integrating fault tree (FT), dynamic Bayesian network (DBN) and support vector regression (SVR). FT graphically describes the logic of mechanism of thermal runaway. DBN allows considering multiple states and uncertain inference for providing quantitative results of the risk evolution. SVR is subsequently utilized for predicting the risk from the DBN estimation. The proposed methodology can be applied for risk early warning of LIB thermal runaway.

Suggested Citation

  • Huixing Meng & Qiaoqiao Yang & Enrico Zio & Jinduo Xing, 2023. "An integrated methodology for dynamic risk prediction of thermal runaway in lithium-ion batteries," Post-Print hal-04103786, HAL.
    • Handle: RePEc:hal:journl:hal-04103786
    DOI: 10.1016/j.psep.2023.01.021
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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).

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