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V. Bereznychenko

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Open access Jul 2026

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.

T. Mariprasath, Kumaresh S S, Seif Al Bustanji et al. · 0 citations
Open access Aug 2026

A physics-constrained temporal modeling framework for robust short-term wind power forecasting

Accurate short-term wind power prediction plays a critical role in ensuring stable grid operation, effective energy management, and the large-scale integration of renewable energy systems under highly variable wind conditions. Although data-driven and deep learning models have demonstrated promising forecasting capability, many existing approaches suffer from performance degradation during rapid wind fluctuations due to the lack of embedded physical constraints and the high computational complexity associated with recurrent architectures. To address these limitations, this article proposes a Physics-Guided Residual Temporal Convolutional Network (PG-ResTCN) for short-term wind power forecasting. The proposed framework integrates dilated temporal convolutional learning with residual connections to effectively capture multiscale temporal dependencies in wind power time series while avoiding the sequential computation of recurrent neural networks, thereby improving computational efficiency. Furthermore, a physics-based smoothness constraint is incorporated into the training loss function to enforce physically consistent power ramping behavior that reflects the inherent operational dynamics and inertia of wind turbines, reducing unrealistic fluctuations in predicted power outputs. The effectiveness of the proposed model is validated using a large real-world dataset containing approximately 140,161 hourly samples collected from five wind farm locations, including meteorological variables and corresponding turbine power outputs. Comprehensive experiments are conducted by comparing the proposed method with widely used benchmark models, including Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory networks. Results demonstrate that the proposed PG-ResTCN model achieves superior forecasting performance, obtaining a root mean square error of 0.0359, mean absolute error of 0.0296, and R 2 of 0.9759, outperforming all baseline models. The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability. In addition, the proposed framework maintains high computational efficiency and robustness, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operations.

S. Marisargunam, T. Mariprasath, Mohit Bajaj et al. · 0 citations