An Improved Particle Filter Method to Estimate State of Health of Lithium-Ion Battery
Zengyuan Bian, Yan Ma
Abstract
Accurate prediction of the Remaining Useful Life (RUL) of Lithium-ion batteries can ensure the safe and stable operation of electric vehicles (EVs). This paper presents the Multi-scale Extended Kalman Filter (MEKF) to estimate the State of Health (SOH) and the Gauss-Hermite Particle Filter (GHPF) to predict RUL of battery based on the estimated SOH value. First, the Thevenin equivalent circuit model and capacity exchanging model for SOH estimation and capacity degradation model for RUL prediction are built up. Then, the co-estimator of the State of Charge (SOC) and SOH of Lithium-ion battery, named MEKF, is proposed based on the multi-scale theory. Next, based on the output of the SOH estimation, the model parameters in the capacity degradation model are updated by the GHPF method. Finally, the models of Lithium-ion battery are set up in MATLAB to simulate. The simulation results show that the prediction error is less than 5% when using GHPF for RUL prediction.