Data-Driven Machine Learning Prediction of Impact Failure in Cylindrical Lithium-Ion Batteries

The mechanical safety of lithium-ion batteries (LIBs) under dynamic impact has been recognized as a critical concern for electric vehicles. In this study, three experimental dynamic impact datasets of cylindrical LIBs were established through drop-weight tests, with each dataset capturing the effects of indenter geometry, impact repetition, and state of charge (SOC). Using these datasets, six representative machine learning (ML) models—including ANN, SVR, LSTM, TCN, RF, and XGBoost—were evaluated for predicting force–time responses and analyzing failure-related characteristics indicated by the synchronized voltage response. The results indicated that ensemble models (XGBoost and RF) provided the highest predictive accuracy (R2 > 0.999) under the tested conditions, while temporal models (LSTM and TCN) effectively captured nonlinear time-dependent behavior. These findings demonstrate that ML-based prediction offers a rapid and reliable means for impact-response assessment and voltage-drop-based failure indication in cylindrical LIBs, supporting early-stage safety screening under the investigated impact conditions.