Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method

Abstract

For battery management system, accurate estimation of state of charge (SOC) and state of health (SOH), as well as prediction of remaining useful life (RUL) are of great significance. Herein, backward smoothing square root cubature Kalman filter (BS-SRCKF) is proposed to improve accuracy and convergence speed of SOC estimation. Then the multiscale hybrid Kalman filter (MHKF), which consists of BS-SRCKF and extended Kalman filter (EKF), is employed for the joint estimation of SOC and SOH. Furthermore, improved cuckoo search (ICS) algorithm is embedded in the standard particle filter (PF) to improve its performance, by transferring the particles in the prior distribution region to the maximum likelihood region. Eventually, RUL prediction is achieved based on the SOH information estimated by the joint estimation of SOC and SOH and the improved cuckoo search particle filter (ICS-PF). The simulation results demonstrate that the method for RUL prediction present in this paper has improved accuracy, confidence level and resampling rate compared with two existing methods based on PF and unscented particle filter (UPF).