The development of machine learning-based remaining useful life prediction for lithium-ion batteries

Lithium-ion batteries are the most widely used energy storage devices, for which the accurate prediction of the remaining useful life (RUL) is crucial to their reliable operation and accident prevention. This work thoroughly investigates the developmental trend of RUL prediction with machine learning (ML) algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement directions. The possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this paper. The ten most used ML algorithms for RUL prediction are first identified in 380 relevant papers. Then the general flow of RUL…

• CS
  China Scholarship Council

  Awards: 202108320111, 202208320055