基于UKF的电动汽车锂电池SOC估计方法

准确估计电池的荷电状态(SOC,state of charge)是电动汽车电池管理系统研究的关键技术。基于Thevenin模型建立了状态空间方程组,采用无色卡尔曼滤波(UKF,unscented Kalman filtering)算法实现非线性条件下的SOC准确估计。硬件在环仿真试验表明:UKF估计误差小于5%,且当SOC值低于50%时,其估计结果明显优于扩展卡尔曼滤波(EKF,extended Kalman filtering)方法,有较高的实用价值。

Method of State of Charge Estimation Based on Unscented Kalman Filtering For Li-Ion Battery of Electric Vehicle

Accurate estimation of state of charge(SOC) is one of the key technology in the research of battery management system(BMS) of electric vehicle.Choosing SOC as state variable of the system,state space equations are established based on the research of Thevenin model,then unscented Kalman filtering(UKF) algorithm is utilized to estimate SOC accurately.A hardware-in-the-loop simulation test shows that the error of UKF estimation is less than 5%,especially when the SOC is under 50%,the result of UKF estimation ...