Advanced Lithium-Ion Battery Prognostics: A Comprehensive Review of Machine Learning Approaches for Remaining Useful Life Prediction - Kamlesh Sharadchandra Mahajan; Nitish Kumar Gautam (International Journal of Mechanical Dynamics and Systems Analysis, 2025)
The lithium-ion battery (LIB), as one of the main sources for portable power systems, has been increasingly popular owing to its widespread applications in electric vehicles, consumer electronics, aerospace and renewable energy. Despite their advantages in high energy density and long cycle life, LIBs suffer from degradation over time of aging and cycling, resulting in loss of performance, safety issues, and economic bottlenecks. Predicting their Remaining Useful Life (RUL) is critical for proactive maintenance, cost savings, and prolonging the operational life span. Classical physics-based models face a challenge to capture the complex nonlinear degradation patterns of LIBs, which stem from multiple coupled electrochemical processes like SEI formation, lithium plating, and mechanical stress and electrolyte decomposition. These are compounded by external influences such as temperature, depth of discharge and charge rates. Machine Learning (ML) provides an attractive alternative by learning degradation mode from data without using explicit physical equations. Models such as SVR, Random Forest (RF), Gaussian Process Regression (GPR) and deep learning models like LSTM, CNN, hybrid networks have achieved great success in estimating RUL. The emerging trends are Transfer Learning, Survival Analysis and Explainable AI (XAI) for generalization, reliability and interpretability of the models under a wide diversity of chemistries and real-world conditions. This is all done despite outstanding difficulties such as modelling the capacity recovery, uncertainty quantification and varying cell form factors. Such frameworks as Battery ML provide a glimpse of how domain-aware engineering is already beginning to integrate with AI, evolving toward intelligent lifecycle management of next-gen battery systems.
Keywords: Lithium-ion batteries (LIBs); Machine learning (ML); battery degradation mechanisms; explainable artificial intelligence (XAI); remaining useful life (RUL) prediction