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

Huixing Meng, Mengqian Hu, Ziyan Kong, Yiming Niu, Jiali Liang, Zhenyu Nie and Jinduo Xing

Reliability Engineering and System Safety, 2024, vol. 251, issue C

Abstract: 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, we establish physics-informed and data-driven BNs. Subsequently, we bridge physics-informed and data-driven BNs to establish a Bayesian network for risk analysis of LIB accidents. Second, we set up safety barriers in the system, including detectors, emergency response, and firefighting facilities. Third, we evaluate the performance of safety barriers. We 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, we 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.

Keywords: Lithium-ion battery; Fault tree; Bow-tie; Data-driven Bayesian network; Risk analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024003661

DOI: 10.1016/j.ress.2024.110294

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