Research On State Of Charge Estimation Of Battery Management System For New Energy Vehicles

With the global electric vehicles ownership increasing,the study on electric vehicles is intensified.Since the battery,as the energy power of electric vehicles,has a great impact on the performance and safety of electric vehicles,it needs to be monitored and managed properly.Battery management system(BMS)is an important component to achieve those functions in which the estimation of state of charge(SOC)is the core and basic function of the BMS.Due to the nonlinear characteristics of battery and the complexity of vehicle operation conditions,the estimation of battery's states is a challenge.This paper focuses on the estimation of battery SOC,and the algorithm of joint estimation of battery state and parameters.The goals are to improve the accuracy of battery SOC estimation and the stability of battery SOC estimation algorithm,reduce the calculation cost and enhance the adaptability of the algorithm at different temperatures.Specifically,the following studies are carried out in terms of power battery tests,modeling and parameter identification,multi-scale power battery state and parameters joint estimation,stability analysis of battery state and parameters joint estimation algorithm,global sensitivity analysis of battery model parameters and battery SOC estimation with respect to temperature effects:(1)The battery test platform is set up,where the battery test process is designed,and the battery model is established where the battery parameters can be identified using the battery test data.Through the battery tests,the data under different battery operation conditions required for the algorithm's verification are acquired.By conducting the system identification,the parameters of the battery model can be obtained,which are essential for the algorithm design.(2)A multi-scale parameter adaptive SOC method is designed for the joint estimation of battery state and parameters.The direct estimation of battery open circuit voltage(OCV)is transformed into the estimation of OCV fitting parameters while the fast time-varying parameter of the battery OCV are transformed into the slow time-varying parameter.The multi-scale estimation of all parameters in battery model including battery OCV is implemented,where the higher accuracy of battery SOC estimation is achieved.Experimental results show that the proposed approach for estimation of OCV can effectively improve the accuracy of SOC estimation.(3)Aiming at solving the problem of divergence and instability in the joint estimation algorithm of battery state and parameters,an adaptive SOC estimation algorithm based on dead zone is proposed.The dead zone is defined by the absolute value of the difference between the terminal voltage calculated by the battery model and the measured terminal voltage,and a dead zone based adaptive SOC estimation algorithm is designed.The experimental results show that the stability and accuracy of the SOC estimation algorithm are improved,where the errors generated by the dead zone parameter adaptive SOC estimation algorithm are less than 1%.(4)The influence of each battery model parameter on SOC estimation errors is studied,and an approach of a global parameter sensitivity analysis for equivalent circuit model is proposed.The parameters space of battery model is established by Monte Carlo method.The extended Kalman filter algorithm is adopted as the benchmark algorithm of SOC estimation,and the dynamic condition is used as the working condition input to calculate the root mean squared error(RMSE)of SOC estimation corresponding to each group of battery model parameters.The sensitivity value of each battery parameter relative to the RMSE of SOC estimation is calculated by correlation method and standard regression coefficient method.The simulation results show that the internal resistance and OCV of the battery are the highest sensitivity parameters that affect the battery SOC estimation errors.Based on the results of parameters sensitivity analysis,a battery SOC estimation algorithm is designed that only accounts for the higher sensitivity parameters.Experimental results show that the proposed higher sensitivity parameters adaptive SOC estimation algorithm can not only ensure the accuracy of SOC estimation,but also reduce the calculation cost.(5)Aiming at addressing the influence of temperature on battery performance,a method of SOC estimation with temperature effect is designed.According to the influence of temperature on the battery model parameters,different treatment methods are adopted.The fitting formulas of temperature to battery capacity and temperature to battery OCV are established.The battery impedance is affected by temperature,SOC and discharge rate.So,the parameter estimator is used to estimate these parameters jointly to overcome the influence of temperature and reduce the off-line calibration of the internal resistance and the battery impedance.Experimental results show that the battery SOC at different temperatures is estimated with high accuracy.The maximum error of SOC estimation can be kept within 2% under different temperatures.