Remaining life prediction of lithium-ion batteries based on health management: A review

Lithium-ion battery remaining useful life (RUL) is an essential technology for battery management, safety assurance and predictive maintenance, which has attracted the attention of scientists worldwide and has developed into one of the hot issues in battery systems failure prediction and health management technology research. This paper focuses on developing a Lithium-ion battery remaining practical life prediction algorithm to improve its adaptability and accuracy. To achieve this goal, the fusion model methods based on data-driven, model-driven and the combination of the two are summarized, and the problems they face are discussed. Accurate estimation of the remaining life of lithium batteries not only allows users to obtain battery life information in time, replace batteries that are about to fail, and ensure the safe and efficient operation of the battery pack but also ensures that lithium-ion batteries are used as the primary energy supply and energy storage to a large extent. The safety and reliability of the equipment in its operation avoid accidents and reduce operating costs. It focuses on the methods and research status of lithium-ion battery remaining life prediction at home and abroad and the main factors affecting battery life and prediction accuracy. In this paper, the advantages and limitations of various prediction methods are summarized and compared, the current technical research difficulties are outlined, the urgent problems to be solved, and the development trend of battery life prediction technology research are given.