Research on SOC estimation based on second-order RC model

Tiezhou Wu, Lunan Liu, Qing Xiao, Quan Cao, Xieyang Wang


The estimation accuracy of batteries’ State of Charge (SOC) plays an important role in the development of hybrid electric vehicle (HEV). Accurate estimation of SOC can prevent battery from overly charging and discharging, so the lifetime of batteries will be increased. Although Kalman filter algorithm has better estimation accuracy for HEV application in which the current changes fast, Kalman filter algorithm deeply relies on the battery model. In other words, the accuracy of batteries’ SOC estimation needs precise batteries models. Besides, when the HEV is running, the statistical characteristics of noise produced in the course of the battery management system collecting data are unknown. This can cause estimated performance of Kalman filter algorithm to decrease even diffuse. To solve the problem, adaptive Kalman filter algorithm is adopted to estimate battery SOC based on the second order RC battery model in this paper. Through MATLAB simulation analysis, the estimation accuracy of battery SOC is improved to some extent.



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