Hybrid temporal deep learning and ensemble regression framework for remaining useful life prediction of lithium-ion batteries in energy storage systems
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is essential for enhancing the reliability, operational safety, and energy management efficiency of electric vehicles and modern energy storage systems. However, battery degradation is governed by highly nonlinear electrochemical mechanisms and complex temporal dependencies that are difficult to model using conventional physics-based approaches or standalone machine learning techniques. To address these challenges, this article proposes a hybrid data-driven framework integrating a Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Extreme Gradient Boosting (XGBoost) for accurate LIB RUL prediction. The proposed architecture utilizes the TCN module to capture short-term temporal degradation patterns from sequential battery operational data, while the BiLSTM network learns long-term temporal dependencies and degradation evolution across multiple charge–discharge cycles. The deep temporal representations extracted by the TCN–BiLSTM network are subsequently processed using an XGBoost regression model to effectively model nonlinear relationships between battery operational characteristics and RUL. The framework is validated using the NASA LIB aging dataset containing 14,896 charge–discharge cycle samples with operational features including cycle index, discharge time, charging duration, voltage degradation characteristics, and constant-current charging behavior. Statistical analysis and Min–Max normalization are employed to improve feature consistency, numerical stability, and model convergence. Experimental results demonstrate that the proposed framework effectively captures battery degradation dynamics and achieves highly accurate and stable prediction performance. Five-fold cross-validation results yield a low Mean Absolute Error of 0.00957, Root Mean Square Error of 0.02926, and a high coefficient of determination ( R 2 ) of 0.9882, indicating excellent predictive capability and strong generalization performance. Comparative analysis further demonstrates that the proposed hybrid framework outperforms conventional Random Forest, XGBoost, LSTM, and BiLSTM models in terms of prediction accuracy and robustness. In addition, ablation analysis confirms the complementary contribution of temporal convolutional learning, sequential dependency modeling, and ensemble nonlinear regression toward improved RUL estimation. The proposed framework provides a robust and computationally efficient solution for intelligent battery health monitoring, predictive maintenance, and smart battery management applications in electric vehicles and energy storage systems.