TY - JOUR
T1 - Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks
AU - Meng, Huixing
AU - Hu, Mengqian
AU - Kong, Ziyan
AU - Niu, Yiming
AU - Liang, Jiali
AU - Nie, Zhenyu
AU - Xing, Jinduo
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/11
Y1 - 2024/11
N2 - 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.
AB - 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.
KW - Bow-tie
KW - Data-driven Bayesian network
KW - Fault tree
KW - Lithium-ion battery
KW - Risk analysis
UR - http://www.scopus.com/inward/record.url?scp=85198375980&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2024.110294
DO - 10.1016/j.ress.2024.110294
M3 - Article
AN - SCOPUS:85198375980
SN - 0951-8320
VL - 251
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 110294
ER -