Research And Implementation Of SOC Estimation Method For Power Battery In Electric Vehicle

With the increasing number of electric vehicles in the global automotive market,power batteries have become the focus study at present.The accurate estimation of State of Charge(SOC)is one of the key technologies of Battery Management System(BMS)for electric vehicles.The accurate SOC estimation can not only improve the utilization of power batteries,but also be of great significance to the control decision and safety of the whole vehicles.Starting with the working characteristics of power batteries,this paper analyses the influence factors of SOC estimation.On the premise of power battery modeling and parameter identification,a SOC estimation algorithm based on Extended Kalman Filter(EKF)is proposed.The main research work in this paper is as follows:1.According to the internal structure of lead-acid power battery,its electrochemical reaction principle and working characteristics were studied.The working characteristics of lead-acid power battery were tested.The voltage response characteristics of power battery under constant current discharge and dynamic current discharge were analyzed.The relationship curve between SOC and terminal voltage,internal resistance and capacity characteristics of power battery under different discharge rates were obtained.Based on the analysis of the working characteristics of batteries,the main factors affecting the accuracy of SOC estimation are summarized.2.Based on the analysis of various power battery models,the Thevenin equivalent circuit model is established according to the working characteristics of power battery.The identification formula of parameters is given,and the pulse discharge experiment of battery is designed.The least square method with genetic factor is used to identify the parameters of battery model.Finally,the simulation is carried out by using MATLAB.The simulation results show that the Thevenin model high identification accuracy and can accurately describe the working characteristics of power batteries.3.Based on the Extended Kalman Filter,the SOC is estimated by combining the estimation algorithms of the time-integration method and the open-circuit voltage method,and the estimation accuracy is further improved by dynamically correcting the observation error and the real-time capacity.The simulation results show that this algorithm has good correction accumulative error and initial error ability,and can accurately estimate the residual power of power battery.4.The data acquisition module,SOC estimation module and CAN bus communication module are integrated into the battery management system,and the master computer interface is designed.A test platform is built with programmable electronic load to verify the SOC estimation algorithm.The experimental results show that the SOC estimation algorithm designed in this paper has the advantages of fast convergence and good robustness in the battery management system,and the estimation accuracy of battery SOC is less than 6%,which meets the design requirements.