Remaining useful life prediction of lithium-ion battery based on chaotic particle swarm optimization and particle filter

The remaining useful life (RUL) prediction of lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. Due to the complexity of the battery aging mechanism, the accurate prediction of RUL by traditional methods is difficult to guarantee. To improve the prediction performance of the particle filter (PF), an improved particle filter based on the chaotic particle swarm optimization algorithm (CPSO-PF) is presented. Then it is applied to predict the RUL of lithium-ion batteries. First, for a better posterior estimate in the PF, CPSO is used to drive the prior distribution of the particles toward a high likelihood probability to obtain a better -proposed distribution, which helps overcome the problem of degeneracy and impoverishment of particles. Then, Three models were employed to track the degradation trajectory of the batteries, including PF?the extended Kalman particle filter (EKPF), and CPSO-PF. Finally, the RUL of lithium-ion batteries was predicted with the three models. The experimental results demonstrate that CPSO-PF has higher prediction accuracy and strong robustness.