Comparison and Analysis of SOC Estimation Based on First-Order and Second-Order Thevenin Battery Models Based on EKF

Abstract

The accurate modeling and estimation of state-of-charge (SOC) of lithium-ion batteries are of great significance for their utilization efficiency, service life extension and battery management system (BMS) design. This paper aims to provide understanding and guidance for understanding and guidance on different battery models and their parameter selection by comparing and analyzing the performance differences of Extended Kalman Filter (EKF) algorithm based on the first-order Thevenin battery model and the second-order Thevenin battery model. The Battery module in Matlab/Simulink is used to simulate the HPPC (HybridPulse Power Characterization) test of real batteries, and the model is parameterically identified. By establishing Thevenin battery models of different orders and applying the EKF algorithm for SOC estimation, the differences in accuracy, robustness and computational complexity of the two models are compared and analyzed, and experimental verification is carried out under different working conditions.