A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter

Abstract

The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator to represent the degradation of lithium-ion battery, and through prediction of battery capacity, the remaining useful life (RUL) of battery can be estimated. Quite a few effective methods have been developed for predicting the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries, and particle filtering (PF) is one of them. In this paper, a novel PF-based method for RUL estimation of lithium-ion batteries is developed combining Kalman filter and particle swarm optimization (PSO). First, the standard PF is combined with Kalman filter to increase the accuracy of estimation, and then a particle swarm optimization algorithm is integrated to slow down the particle degradation due to particle resampling. The battery dataset provided by NASA is used to verify the proposed approach. RUL prediction results compared with standard PF and particle swarm optimization-based PF demonstrates the higher accuracy of our proposed method.