SOC Estimation Algorithm Of Lithium-ion Battery For Electric Vehicle

With car ownership increasing pressure on resources and the environment caused by energy consumption and emissions increasing surge of electric vehicles with its advantages of green, low-carbon energy is receiving more and more attention. As an important part of the battery electric vehicles, which are the main parameters of the state of charge of the battery management system(BMS), vehicle control system according to the SOC value provided to determine the BMS control strategy, so SOC estimate is accurate or not has an important impact for the vehicle control. Car batteries due to major changes in the working environment temperature, current volatility, to bring a certain degree of difficulty to estimate SOC.Object of study is the SOC estimation method of lithium iron phosphate, in-depth study of the impact of the battery SOC estimation accuracy of the model and SOC estimation method. This paper describes the structural characteristics and working principle of lithium iron phosphate, the major factors that affect battery performance were analyzed by voltage, resistance, capacitance, and other basic characteristics of the battery experimental study of the battery, the battery model for the establishment of the foundation.For pure electric vehicle lithium-ion battery state of charge and discharge current at ambient temperature changes less accurate estimation problem under large, extended Kalman filter(EKF) algorithm uses an improved Thevenin model. The battery performance model, the battery capacity as state variables, the effect of the SOC estimation accuracy as ambient temperature and discharge current correction, adaptive extended Kalman filter algorithm to improve the SOC estimation accuracy.