Battery remaining useful life prediction algorithm based on support vector regression and unscented particle filter

Abstract

Batteries are used in many areas, such as the electronic, aeronautics, astronautics, automobile and energy, etc. However, there are many explosion and fire accidents caused by the battery aging, so accurately estimating its remaining useful life (RUL) is very critical. In this paper, an improved method is proposed by using support vector regression-unscented particle filter (SVR-UPF), which increases the accuracy of the RUL prediction results. Firstly, an exponential model is adopted to approximately express the degeneration of battery capacity. Secondly, a novel SVR-UPF method is presented to solve the degeneracy phenomenon of the UPF algorithm, and then it is applied to predict the battery RUL. Finally, some experiments and comparisons have been done to validate the improved SVR-UPF prediction method. The results show that the proposed method is better than the standard particle filter (PF) prediction method and the standard unscented particle filter (UPF) prediction method.