Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks

The catastrophic consequences of lithium-ion battery (LIB) accidents have attracted high attention from society and industry. Accordingly, risk analysis is indispensable for the risk prevention and control of LIBs. Nevertheless, it is difficult to establish a physics-informed risk analysis model due to the complex material characteristics and aging mechanisms of LIBs. Meanwhile, the data-driven approach requires historical information of LIBs and does not merely rely on knowledge of the internal mechanisms of LIBs. This study proposes a method integrating the physics-informed Bayesian network (BN) (i.e., mapping from fault tree) and data-driven BN (i.e., learning from data) to conduct risk analysis of LIBs. First, the authors establish physics-informed and data-driven BNs. Subsequently, they bridge physics-informed and data-driven BNs to establish a Bayesian network for risk analysis of LIB accidents. Second, they set up safety barriers in the system, including detectors, emergency response, and firefighting facilities. Third, they evaluate the performance of safety barriers. The authors validate the proposed model using data from LIBs in air transportation. The results indicate that safety barriers can reduce the accidental risk of LIBs. Eventually, they propose suggestions for the risk control of LIBs in air transportation. This study is supposed to provide theoretical basis for the risk prevention and control of LIB accidents.

• Record URL:
  • https://doi.org/10.1016/j.ress.2024.110294
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0951832024003661
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Meng, Huixing
  • 0000-0002-6487-890X
  • Hu, Mengqian
  • Kong, Ziyan
  • Niu, Yiming
  • Liang, Jiali
  • Nie, Zhenyu
  • Xing, Jinduo
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 110294
• Serial:
  • Reliability Engineering & System Safety
  • Volume: 251
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0951-8320
  • Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety

Subject/Index Terms

• TRT Terms: Air transportation; Aviation safety; Bayes' theorem; Hazards; Lithium batteries; Risk analysis
• Subject Areas: Aviation; Energy; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01933074
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 9 2024 3:17PM