Aimed at the problems that the capacity of a lithium-ion battery is difficult to obtain in the direct prediction of its remaining useful life (RUL) and the characteristic parameters are redundant or insufficient in the indirect prediction, an indirect prediction method for the RUL of lithium-ion battery based on the data preprocessing technology and improved support vector regression (SVR) is proposed. First, multiple indirect characteristic parameters are extracted at the battery charging and discharging stage. Second, principal component analysis is used to remove the redundancy of various parameters, and a fusion health indicator (HI) with sufficient information is reconstructed. Third, a fusion HI prediction model and a capacity prediction model based on the whale optimization algorithm and SVR are built, and the complete ensemble empirical mode decomposition with adaptive noise is used to decompose the fusion HI into several modal components. Each component is input into the fusion HI prediction model for HI prediction, and the prediction results are superimposed into the capacity prediction model to realize indirect RUL prediction. Finally, the NASA battery degradation data set is used for verification, and results show that the mean absolute error and root mean square error of RUL prediction results for the proposed method are controlled within 1.78% and 2.5%, respectively, which effectively improves the prediction accuracy of RUL. The research provides a new idea for the RUL prediction of lithium-ion batteries.