A comprehensive review of remaining useful life prediction methods for lithium-ion batteries: Models, trends, and engineering applications

Abstract

Under complex working conditions, accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems, the safe driving of electric vehicles, and the continuous power supply of electronic devices. This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field. First, the battery degradation mechanisms and lightweight data acquisition are analyzed. Secondly, a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods, and the application characteristics and implementation limitations of different methods are analyzed in detail. An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction. Then, collaborative modelling of calendar ageing and cyclic ageing is discussed, revealing their coupled effects and corresponding RUL prediction methods. Finally, the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified, potential solutions are proposed, and the future development trends are outlined.