Model Parameters Online Identification and SOC Joint Estimation for Lithium-Ion Battery Based on a Composite Algorithm

Abstract

The purpose of this paper is to discuss how to solve the problem of on-line identification of model parameters of Li-ion battery and on-line estimation of SOC. Based on the matlab/simulink platform, a first-order RC equivalent circuit model of the battery is built, and a joint estimation algorithm of the model parameters and SOC of the lithium ion battery is designed based on the dynamic model, which is compared with the single adaptive Kalman filter algorithm (AEKF). The simulation results show that the proposed joint estimation algorithm can make effective online estimation and update of the battery model parameters and SOC. The average estimation error of SOC is less than 2.8%, the estimation accuracy is higher than that of adaptive Kalman filter, and its robustness level is relatively high.