SOC estimation of lithium battery based on EKF and UKF

Accurate SOC estimation plays a central role in lithium battery applications. It not only affects operational safety, but also determines energy utilization efficiency and battery life cycle length. Aiming at the lack of accuracy of the traditional SOC estimation method under dynamic working conditions, this paper adopts two algorithms, Extended Kalman Filter (EKF) and Untraceable Kalman Filter (UKF), based on the second-order RC equivalent circuit model of Li-ion battery to estimate the SOC of the battery. By comparing and analyzing the estimation error and convergence performance of the two filtering algorithms, the results show that both EKF and UKF have good dynamic response capability and high estimation accuracy, among which UKF performs better in terms of nonlinear processing and estimation stability. It provides theoretical basis and technical support for the precise control and optimization of battery management system (BMS).