BCRLS-EKF-Based Parameter Identification and State-of-Charge Estimation Approach of Lithium-Ion Polymer Battery in Electric Vehicles

摘要

This paper built the Thevenin battery model in MATLAB/Simulink, and to determine the ohmic resistance of the battery polarization resistance, polarization capacitance through off-line parameter identification method. The model was verified by continuous charging and discharging, HPPC and DST operating condition. The results show that the model has a high precision, and the terminal voltage error is only 10 mV, which fully meets the actual demand. In order to solve the problem that the traditional RLS algorithm has a large error in the identification of the battery parameters under colored noise, we propose to use BCRLS algorithm to identify the parameters of the battery, which can effectively eliminate the colored error and improve the recognition accuracy. The BCRLS algorithm is combined with the EKF algorithm to estimate the SOC accurately based on the accurate estimation of the model parameters. In order to verify the reliability of the algorithm, the corresponding algorithm model was built in MATLAB/Simulink. The comparison between the DST and off-line results shows that the absolute error of the estimated SOC value is less than 1%, which can fully meet the needs of the BMS.