A comprehensive review of lithium-ion battery remaining useful life prediction: methodologies, datasets, performance metrics, and future perspectives

Abstract Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is crucial for enhancing the safety, reliability, and efficiency of battery-powered applications like electric vehicles and energy storage systems. This review analyzed over 200 peer-reviewed studies and categorized RUL prediction methods into three major approaches: physics-based, data-driven, and hybrid models. Hybrid models, which combine physical insights with data-driven methods, are the most widely used due to their adaptability, accuracy, and interpretability. Data-driven models, such as long short-term memory and convolutional neural networks, excel in capturing complex, nonlinear relationships but require large datasets and high computational power. While physics-based models offer high accuracy, they are less commonly employed due to their complexity and extensive parameter tuning requirements. Despite their benefits, hybrid models face challenges, including increased computational complexity and integration difficulties. This review also highlights key datasets and evaluation metrics used in LIB RUL prediction. The NASA dataset is the most frequently used, appearing in 30.8% of the papers, followed by the CALCE dataset. Root mean square error is the most common evaluation metric, used in 29.6% of the studies, followed by mean absolute error and mean absolute percentage error, which are essential for assessing prediction accuracy. Through comparative analysis, this review identified key challenges and outlined future research directions, including the need for lightweight hybrid models, standardized benchmarking datasets, and uncertainty-aware evaluation frameworks to support real-time, robust battery management systems. In conclusion, the future of LIB RUL prediction lies in the integration of advanced hybrid models, improved datasets, and uncertainty-aware performance metrics, with a focus on refining data-driven approaches for handling real-time, multi-sensor data.