Crash Failure Prediction of Lithium-ion Batteries Based on Finite Element and Machine Learning Methods

Abstract The aging state and operational environment of lithium-ion batteries (LIBs) in electric vehicles are highly complex and variable. To investigate LIB safety under foreign object collisions, this study develops a detailed finite element model of 18650 LIBs at different cycle counts. Following model validation, we conduct comprehensive simulation tests using indenters of varying types, sizes, intrusion angles, and loading positions. A machine learning model is subsequently developed to rapidly predict battery failure displacement and load. Results demonstrate that this approach achieves high-accuracy prediction of LIB failure behavior, providing a valuable reference for other LIB application scenarios.