Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge - Priyanka Sarang Patil; Deepak Shankar Raskar; Mukesh Kumar Gupta (International Journal of Electro-Mechanics and Material Behaviour, 2026)
Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. To address these challenges, this study proposes a hybrid predictive framework based on multimodal data fusion and advanced machine learning, integrating electrical, thermal, and degradation-related indicators to capture both short-term operational dynamics and long-term material aging characteristics. Multiple deep learning architectures, including LSTM, GRU, CNN, and CNN–LSTM, are evaluated, and a hybrid CNN–LSTM-based fusion model is developed to enhance feature representation and decision reliability. Validation using publicly available lithium-ion battery datasets demonstrates that the proposed approach achieves superior performance, with a classification accuracy of 94.8%, an R² value of 0.96 for SoC prediction, and 0.94 for RUL estimation. The results confirm that multimodal data fusion combined with hybrid learning architectures effectively reduces prediction uncertainty and improves generalization across different aging profiles. This work provides a data-driven tool for battery material degradation assessment, manufacturing quality control, and predictive maintenance of energy storage systems used in industrial, renewable, and electric mobility applications.
Keywords: Hybrid Machine Learning; Li-Ion Battery Prognostics; Multimodal Data Fusion; Remaining Useful Life Prediction; State of Charge Estimation