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.
Ponkumar Ganesapandiyan, P. Hemachandu, N. Rajavinu et al.· International Journal of Pow...· 0 citations
The prediction of lithium-ion battery capacity degradation plays a vital role in ensuring safe and efficient operation in electric mobility and renewable energy applications. This paper evaluates standalone machine learning, deep learning, and hybrid models for battery capacity estimation. The evaluated ML models include random forest, gradient boosting, and extreme gradient boosting (XGBoost), while the DL model employs a multilayer perceptron. The hybrid framework combines DL based feature extraction with ensemble ML regression or classification. A real-world dataset comprising temperature, resistance, reactance, and battery type was preprocessed, scaled, and divided into training and testing subsets. Hyperparameter tuning, k-fold cross-validation, and uncertainty quantification were incorporated to improve reliability and reproducibility. Model performance was assessed using RMSE, MAE, and R² for regression and receiver operating characteristic–area under the curve (ROC-AUC) and F1-score for classification. ROC curves, calibration curves, metric-comparison charts, cycle-wise degradation plots, and residual analyses were used for evaluation. Results demonstrate that the hybrid model outperforms standalone approaches by reducing RMSE and improving calibration, reliability, uncertainty alignment, and interpretability. This also establishes its novelty over existing state of health (SOH) models and highlights future extensions involving LSTM-based temporal modeling and chemistry-adaptive transfer learning. Overall, hybrid modeling provides a promising solution for reliable predictive battery maintenance.
Shobana Devendiren, A. Muthuraman, M. Vanitha et al.· International Journal of Pow...· 0 citations
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