Predicting Cycle Life for Lithium-Ion Batteries with Ternary Cathode Materials Using Data-Driven Machine Learning

Lithium-ion batteries with ternary cathode materials offer several advantages, including high energy density, relatively low cost, and high power density, making them suitable for applications in electric vehicles and large-scale grid storage systems. However, a significant challenge is the rapid and nonlinear capacity fade during cycling, which necessitates accurate predictions of battery cycle performance. In this study, we developed three machine learning models, namely Elastic Net, Random Forest, and XGBoost to predict the remaining useful life (RUL) of batteries with ternary cathodes using data from a public database. XGBoost demonstrated the highest prediction accuracy when tested with training data from the first 100 cycles, achieving a prediction error of 11.8%. Furthermore, the prediction error increased slightly to 17.0% when tested with only the first 30 charge/discharge cycles. This study exemplifies the potential of machine learning models for predicting battery cycle life, with important implications for the operation and maintenance of electric vehicles and large-scale grid storage systems.