State Of Charge (SOC) Estimation Methods For Power Battery In Electric Vehicle

With the problems of environmental pollution, energy crisis and energy security become increasingly prominent, the study of new energy electric vehicle has become the research hotspot. In the battery management system of electric vehicle, the battery state of charge(SOC) is one of the most important parameters for charge/discharge process, balanced management, life and safety of the battery pack. Therefore, accurately estimate the battery SOC can effectively improve the efficiency of battery and extend battery life, which is of great significance in the development of electric vehicles.However, the battery SOC is different from voltage and current that can be directly measured by sensors, it is only estimated by other indirect methods. In this paper, the mainly contributions of SOC estimation for electric vehicle power battery are as followings:Firstly, an improved least squares support vector machine(LS-SVM) model is designed in this paper, by which the model parameters are dynamically adjusted and the open-circuit voltage(OCV) for the battery is online estimated. Apere-hour(Ah) integration method is applied to estimate SOC based on the initial SOC value determined by the relationship between SOC and OCV. The OCV deviation is corrected SOC to effectively compensate the fitting errors and the cumulative errors caused by Ah integration method. Simulation results show that the presented algorithm can accurately approximate the actual SOC value with the average absolute error about 1.2793%.Secondly, another SOC estimation method, which combines the constructed controlled auto-regressive and moving average(CARMA) model with feedforward-feedback compensation method used for revising SOC by the deviation of terminal voltage, is presented in this paper. Fully taken into account the measurement errors of voltage and current, 1) the CARMA model is employed to estimate the battery open-circuit voltage(OCV), 2) BP neural network rather than the high order polynomial approximation is used to capture the strongly nonlinear relationship between OCV and SOC with high precision and 3) with the good consistency of the OCV-SOC curve under the process of battery charge and discharge cycles within a certain temperature range, OCV is adopted to estimate SOC.It is a big challenge for OCV-based SOC estimation that the flat area of OCV-SOC curve for lithium-ion power battery enlarges the measurement errors of OCV. By analyzing the flat characteristic of ΔSOC-OCV curve, the feedforward-feedback compensation for SOC is used for improving the accuracy of OCV-based SOC estimation method. Simulation results confirmed the effectiveness of proposed approach that has evidently advantages over other estimation methods.