METHOD AND DEVICE FOR RISK PREDICTION OF THERMAL RUNAWAY IN LITHIUM-ION BATTERIES

One or more embodiments of the present description provide a method and device for risk prediction of thermal runaway in LIB. The method includes: acquiring knowledge of a mechanism for thermal runaway in LIB; describing an evolution process of thermal runaway in LIB by adopting a fault tree; mapping a fault tree structure to a dynamic Bayesian network model for thermal runaway in LIB to obtain quantitative results of a risk of thermal runaway in LIB; and taking the quantitative results of a dynamic Bayesian network as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway. By using the method in the present embodiment, an evolution trend of battery thermal runaway can be predicted by fusing multiple thermal runaway causes and multi-source data, and thus, the prediction results are relatively accurate.