Remaining useful life prediction of lithium-ion batteries based on support vector regression optimized and grey wolf optimizations

Abstract

Remaining useful life(RUL) prediction of Lithium-ion batteries(LIBs) plays an important role in the battery management system, and accurate prediction can ensure the safe and stable operation of the battery. However, an accurate RUL prediction is difficult to achieve. In this paper, a method based on grey wolf optimization(GWO) and support vector regression(SVR) has proposed, which effectively improves the accuracy of LIBs remaining useful life prediction. Since the kernel parameter of SVR is difficult to select, the GWO algorithm is employed to optimize the SVR kernel parameters. This method is verified according to the battery datasets provided by the NASA Prognostics Center of Excellence(PCoE). Compared with the SVR method, the RUL prediction accuracy of the GWO-SVR has been significantly improved. On this basis, compared with the advanced method ALO-SVR, the average relative error of GWO-SVR is reduced by 7.16%. The accuracy of RUL prediction has been effectively improved.