A Critical Review of AI-Based Battery Remaining Useful Life Prediction for Energy Storage Systems

This paper provides a comprehensive review of recent advances in remaining useful life prediction for lithium-ion battery energy storage systems. Existing approaches are generally categorized into model-based methods, data-driven methods, and hybrid methods. A systematic comparison of these three methodological paradigms is presented, with hybrid methods further divided into filter-based hybrids and data-driven hybrids, followed by a comparative analysis of remaining useful life prediction accuracy. The literature analysis indicates that data-driven hybrid methods, by integrating the strengths of physical mechanism modeling and machine learning algorithms, exhibit superior robustness under complex operating conditions. Among them, the hybrid framework combining long short-term memory networks with an eXtreme Gradient Boosting model optimized by the Binary Firefly Algorithm demonstrates the highest stability and accuracy in the reviewed studies, achieving a root mean squared error below 2% and a mean absolute percentage error below 1%. Future research may further enhance the generalization capability of this framework, reduce computational cost, and improve model interpretability.