A Hybrid Ensemble Learning Framework for Accurate Battery Remaining Useful Life Prediction in Electric Vehicles
Keywords:
Battery management system, Bayesian optimization, Electric vehicle, Feature selection, Lithium-ion battery, Remaining useful life, SHAP, XGBoostAbstract
Reliable calculation of the remaining usable life (RUL) of lithium-ion batteries is vital for boosting the reliability, operating safety, and maintenance preparation of electric vehicles (EVs). Since battery degradation is highly nonlinear and is affected by a number of operating parameters, estimating the RUL presents a difficult problem. The paper puts forward an explainable hybrid machine learning approach that combines feature selection, Bayesian hyperparameter optimization, and Extreme Gradient Boosting (XGBoost) to achieve accurate prediction of the battery’s RUL. First, the battery data are preprocessed, and the most relevant health indicators are found through correlation-based screening, mutual information analysis, and Recursive Feature Elimination (RFE) to reduce feature redundancy and enhance model performance. Bayesian optimization is then used to find the best XGBoost hyperparameters, thus improving prediction accuracy while at the same time reducing overfitting. The framework is assessed against Random Forest, Decision Tree, K-Nearest Neighbours, and Naïve Bayes by means of standard performance measures like MAE, MSE, RMSE, and R2. The results show that the optimized XGBoost model offers better prediction accuracy and interpretability, and as such can be considered a practical solution for intelligent Battery Management Systems in next-generation EVs.
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