Study On SOC Estimation Method Of Lithium-ion Battery Based On EKF For Electric Vehicles

As the energy crisis and environmental problems are increasingly serious, governments and auto enterprises around the world pay more attention to the research and development of Electric Vehicles. The battery and battery management system (BMS) are important parts of EV. Estimating state of charge (SOC) exactly has a great meaning to extend the life of battery and enhance the performance of EV. In this thesis, as LiMn2O4 battery being for research object, SOC estimation methods of lithium-ion battery are studied. The specific results are as follows:From the basic principles of lithium-ion battery, based on the related tests, the voltage feature, resistance feature, efficiency feature and circulation feature of lithium-ion battery are discussed; SOC is defined from the dynamic and static perspective, and the factors that affect battery capacity are analysed; Taking the temperature, charge and discharge rate, cycle life and self-discharge and other factors into account, the equivalent circuit model of battery is established; Designing the modified HPPC circulation experiment, considering the different SOC and charge and discharge direction, the parameters of the battery model are obtained using least-square method; The simulation model is established in Matlab, simulation results show that the equivalent circuit model has high precision and can simulate the dynamic characteristics of battery accurately.In this paper, the SOC estimation algorithm of LiMn2O4 battery based on the Extended Kalman Filter (EKF) is studied. Based on the established equivalent circuit model of battery, the calculation process of SOC estimation algorithm is determined based on the EKF iteration. In order to improve the accuracy of SOC estimation, three aspects of improvements have been applied in the algorithm:a constant gain is increased taking into account the entire process; a dynamic gain which increases at the beginning of mutation and decreases rapidly after mutation is set up; correcting the observation error covariance dynamically. And this method is verified by experimental condition, can converge to the true value of SOC quickly and it can solve the difficulty in determining the initial value of the AH integration method.The composition and function of battery testing platform are introduced. By RTW tools in Matlab, codes for the algorithm model are generated automatically and will be integrated into the BMS to do Hardware-in-loop test and bench experiment. Experiment results show that EKF algorithm is easily implemented in engineering, and is relatively accurate in the frequent high-current charge and discharge conditions. Finally, the error sources, deficiencies and improvedment of bench test are analyzed, and the scope of EKF algorithm is given.Taking the operating conditions of electric vehicles into account, EKF algorithm amend Ah method once for PEV, EKF algorithm amend Ah method continuously for HEV is proposed to estimate SOC. Taking the EKF amend Ah method for PEV as example, bench test program is designed and validated. Experiment results show that EKF amend Ah method can both estimate SOC quickly, and avoid large data processing operations. The study of SOC estimation algorithm based on EKF for lithium-ion battery has practical value in BMS.