 This paper provides an overview of the current status of data-driven algorithms for predicting the state of health, SOH, of lithium ion batteries, LIBs. It discusses the acquisition of datasets from the charging and discharging processes, feature extraction and selection, as well as the selection of algorithms. The advantages and limitations of different processing methods and cutting-edge data-driven algorithms are summarized and compared. Finally, potential applications and application methods are discussed.