Abstract: Remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) plays an important role in battery prognostics and health management (PHM). Accurate prediction of RUL can maintain and replace the batteries with potential safety hazards in advance to ensure the safety and reliability of the energy storage system. A method based on ant lion optimization and support vector regression (ALO-SVR) was proposed, which can improve the accuracy of RUL prediction of LIBs. Although the SVR method has advantages in processing small samples and time series analysis, but it has problems in the selection of kernel parameter. Thus, the ALO algorithm was utilized to optimize the SVR kernel parameters. Experimental data simulations were performed using NASA Ames Prognostics center of excellence (PCoE) and center for advanced life cycle engineering (CALCE) battery datasets to verify the proposed method. Compared with the SVR method, the ALO-SVR method can provide more accurate RUL prediction results, effectively improve the accuracy and robustness of RUL prediction of LIBs.