Ensemble Learning-Based Battery Health Estimation: A Comparative Study of RUL, SOC, and SOH Prediction Using Machine Learning Models - C.L, Sravanthi; J.N, Chandra Sekhar (International Research Journal of Multidisciplinary Technovation, 2026)
Lithium-ion batteries are paramount energy storage devices used in modern electric vehicles because of its favourable energy density, light weight and environmental benefits. However, issues related to ageing, degrade mechanism, long term reliability and safety remain critical, particularly under variable operating conditions. Reliable estimation of Remaining Useful Life (RUL), State of Charge (SOC) and State of Health (SOH) is therefore required to support efficient energy management and prevent premature battery failure in electric vehicle applications. Recent years, machine learning algorithms have been increasingly adopted for battery state estimation. Nevertheless, systematic comparison of commonly used algorithms for simultaneously predicting RUL, SOC and SOH are still limited. In this work five machine learning models-Decision Tree (DT), Random Forest (RF), Bagging Regressor (BR), XG Boost (XGB) and an ensemble voting regressor combining Random Forest and XG Boost are implemented and evaluated using Hawaii Natural Energy Institute (HNEI) data set comprising capacity, voltage, temperature and cycle life features. Model performance is assessed using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and R-Square (R2). Ensemble model consistently achieves lower prediction error and improved stability with less computational time compared to individual learners across all estimation tasks. The results indicate that combining tree-based models with gradient boosting improves generalization performance while maintain computational feasibility, making the proposed approach suitable for practical battery management systems deployment in electric vehicles.