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 social attention. Accordingly, risk analysis is indispensable for risk prevention and control of LIBs. However, it is difficult to establish a recognized physics-informed risk analysis model due to the complex material characteristics and aging mechanisms of LIBs. Meanwhile, data-driven approach requires historical information of LIBs and does not rely on knowledge of the internal mechanisms of LIBs. This study proposes a method integrating the physics-informed Bayesian network (BN) (mapping from fault tree) and data-driven BN (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 effectiveness of safety barriers. We validate the proposed model using data from LIBs in air transportation. Our 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 can provide theoretical basis for the risk prevention and control of LIB accidents.