Accurate RUL prediction of EV batteries using random forest and ensemble learning frameworks

Abstract

Precise remaining useful life (RUL) estimation for lithium-ion batteries is essential for improving the safety, reliability, and maintenance of electric vehicles (EVs). This study proposes a random forest (RF)-based ensemble learning framework using the publicly available Hawaii Natural Energy Institute (HNEI) dataset containing 15,064 charge-discharge cycles. Seven degradation-related features, including cycle index, discharge time, voltage decrement, maximum discharge voltage, minimum charging voltage, time at 4.15 V, and constant-current charging duration, are extracted to characterize battery aging. The proposed RF model is compared with linear regression (LR), long short-term memory (LSTM), and attention-LSTM models using MAE, root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). RF demonstrates superior prediction performance, achieving MAE of 5.20, RMSE of 6.83, MAPE of 1.33%, and R² of 0.997. Parity and residual analyses further confirm its strong predictive consistency. The proposed approach provides an accurate, computationally efficient, and interpretable solution for BMS applications, enabling effective battery health monitoring, predictive maintenance, charging optimization, and timely replacement.