Research On On-Line Identification And SOC Estimation Of Lithium-Ion Battery Parameters For Electric Vehicles

Under the dual background of global energy and environmental crisis,vehicle electrification has become the main research direction in the world.This paper took 18650 lithium-ion batteries as the research object and conducted research on the online identification and SOC estimation of vehicle lithium battery parameters.The main work of this paper was as follows:(1)A second-order RC equivalent circuit model is established,the state equation of the battery model is deduced by discretization,and the discretized state equation is obtained.The maximum battery available capacity test,OCVSOC battery calibration experiment,and test experiments under different cycle dynamic conditions are designed,and the experiments are completed.The experimental data analysis was completed by using Matlab,the experimental fitting curve was obtained,and the sixth-order SOC-OCV function relationship was determined.(2)The effect of the forgetting factor on the online parameter identification based on the principle of least squares is analyzed,and the adaptive forgetting factor recursive least-squares method is obtained.Under the DST condition,the accuracy of the online parameter identification of the model is verified by comparing the error between the output terminal voltage and the reference terminal voltage.(3)The improved extended Kalman filter is obtained by optimizing the iterative error and noise adaptation,and the AFFRLS-IEKF joint algorithm is formed.Using the Dynamic Stress Test(DST)and the Federal Urban Driving Schedule(FUDS)The SOC is simulated and analyzed,and the results show that the AFFRLS-IEKF joint algorithm has higher accuracy and better adaptability.