Research on SOC estimation method of hybrid electric vehicles battery based on the grey wolf optimized particle filter

Abstract

In this paper, we propose a method called grey wolf optimized particle filter (GWO-PF) to estimate the state of charge (SOC) of the power battery in hybrid electric vehicles (HEVs). The GWO-PF method combines the particle distribution mechanism with grey wolf optimization to achieve accurate SOC estimation. To begin, we compare four different equivalent circuit models of power batteries and select the second-order RC model (RC2 model) as our research focus. Next, we identify the parameters of the RC2 model online using the recursive least square method with a forgetting factor. Finally, we utilize the identified parameters to implement the GWO-PF method for SOC estimation. We compare the performance of the GWO-PF method with two other commonly used methods: the unscented Kalman filter (UKF) and the particle filter (PF). The results demonstrate that the GWO-PF method achieves high precision in SOC estimation, with a controllable relative error within 3.5%.