Abstract:

In order to solve the problem of difficult to accurately estimate and predict the state of charge (SOC) of power lithium-ion batteries in new energy vehicles,a second-order RC equivalent circuit model is chosen to model lithium-ion battery firstly.MATLAB/cftool toolbox and the forgetting factor recursive least squares (FFRLS) are used to identify the parameters of the lithium-ion battery model.Secondly,the SOC estimation accuracy is improved by the extended Kalman filter (EKF) algorithm.Finally,the battery management system (BMS) experimental platform is built.The simulations and experiments are conducted under the federal urban driving schedule (FUDS) in the United States.The results show that the FFRLS method can improve the lithium-ion battery model accuracy compared with the parameter identification method fitted by the cftool toolbox,and the SOC estimation accuracy is high and the convergence speed is fast,which can verify the accuracy and effectiveness of the second-order RC equivalent circuit model of lithium-ion batteries combined with the EKF algorithm to estimate the SOC of lithium-ion batteries.

Key words: state of charge (SOC), equivalent circuit model, forgetting factor recursive least squares (FFRLS), extended Kalman filter (EKF)