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

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.

Automated summary

In this paper, a methodology for dynamic risk prediction of thermal runaway in lithium-ion batteries is proposed, which integrates fault tree, dynamic Bayesian network, and support vector regression. The proposed methodology can be used for risk early warning of thermal runaway in lithium-ion batteries.