With the advantages of high energy density, low self-discharge rate, and no pollution, lithium batteries have gradually developed into one of the important choices in the energy storage industry. The low reliability, high complexity and low accuracy of the battery life assessment model are important factors restricting its application in large-scale energy storage power stations and electric vehicles. In this paper, based on the cyclic aging test data of lithium iron phosphate storage batteries, a residual life prediction method for lithium batteries that incorporates multiple health factors is summarized by analyzing the relationship between the charging current, voltage, and temperature profiles and the battery capacity degradation during the battery aging process. The method combines the time variation corresponding to the same current during charging, the time variation corresponding to the same voltage during discharging, and the moment movement characteristics corresponding to the temperature peak, and utilizes Pearson correlation coefficient and Spearman's rank correlation coefficient to study the linear correlation relationship between the multiple factors and the battery life degradation, and combines with the empirical model of linear regression to achieve a more accurate prediction of the battery life.