Electric Car Lithium Battery SOC Estimation Study

Because of oil resources shortage and air quality degradation, the government enterprises around the world are paying attention to development and research of EV (Electric Vehicle) which has emerged as the main trend in automobile industry. Electric vehicle battery is the energy provider, its status and parameters are not only the main indicator of battery performance but also a key factor in promoting the healthy development of Electric vehicle. SOC (State of Charge) estimation has always been the core component in battery management system, which can provide judgment basis to vehicle control strategy. This paper takes the LiFeO4polymer power battery as the research object, and uses Kalman filter correction algorithm for battery pack online SOC estimation.First, this paper describes the development of electric vehicles background, trends and car battery performance requirements, through the analysis of the working principle and characteristics of the lithium iron phosphate, summed up the effect factors and study the difficulties of online accurate estimation. After comparing some commonly used methods and considering the electric vehicle environment, this paper proposes a new method named Kalman filter correction algorithm on the basis of SOC definition, and using measured data and MATLAB simulation to analyze under laboratory conditions. This method avoids the disadvantages of A-H measurement method, which is used to reduce the measurement error. A-H measurement method as the state expression of extended Kalman filter equations, its advantages and strengths of the extended Kalman filter algorithm combined to further improve the estimation accuracy.The results show that the extended Kalman filter algorithm for real-time online lithium battery SOC estimation is effective, able to more accurately calculate the SOC.The experiment result showed that estimation result of SOC of battery used by the method is consistent with measured values and the method can applied to battery management system.