Research on Modeling and SOC Estimation of Lithium Iron Phosphate Battery at Low Temperature

The battery model is the basis for battery status estimation, and its accuracy will have a direct impact on accuracy of status estimation. In the field of rail transit, the reasonable allocation of battery capacity in conjunction with the actual operating conditions of the train also requires to establish an accurate battery model. Current battery models rarely consider the effect of temperature on model parameters. However, in some areas, it is very likely to encounter extremely cold conditions while the train is in motion. Firstly, taking into account the effects of temperature on available battery capacity, open-circuit voltage, ohm resistance, and polarization parameters, this article constructed a new battery model suitable for low temperature and small rate discharge conditions based on the lithium iron phosphate battery that used in the project. Then, this paper built a battery model in Matlab/Simulink and verified the accuracy of it through simulation. After that, this paper used extended Kalman filter (EKF) algorithm to estimate the state of change (SOC) at different temperatures and carried out simulation verification. Simulation results showed that the battery model and the SOC estimation method established in this paper had higher estimation accuracy in low-temperature environment.