一种基于UKF的SOC估算方法

A method of SOC estimation based on UKF

随着新能源汽车市场规模的增长,电池管理系统(Battery Management Systems,BMS)的市场需求也进一步扩大。作为保障电池安全及延长电池寿命的BMS而言,动力锂电池组的荷电状态(State of Charge,SOC)估算是BMS研究的重点。在研究了安时积分法估算SOC时受SOC初始值影响较大,且具有累积误差的问题,以及扩展卡尔曼滤波算法(EKF)估算SOC时收敛较慢的基础上,采用二阶RC等效电路模型对锂电池进行建模分析,针对锂电池各参数受SOC变化的影响,引进无迹卡尔曼滤波(UKF)算法,给出了锂电池的SOC仿真实验。实验结果表明,该种基于UKF的估算方法对SOC的估算更准确,误差更小且收敛速度快,对传统采用定值电池参数BMS的改进具有重要意义。

With the growth of the new energy vehicle market,market demand for battery management systems(BMS)has also further expanded.For BMS,which guarantees battery safety and prolongs battery life,the estimation of the state of charge(SOC)of a powered lithium battery pack is the focus of BMS research.Based on the study of the problem that the AH integration method is greatly affected by the initial value of the SOC and has a cumulative error,and the extended Kalman filter algorithm(EKF)has a slower convergence when estimating the SOC,a second-order RC equivalent circuit model was used to model and analyze the lithium battery.Considering that the parameters of the lithium battery are affected by SOC changes,the Unscented Kalman Filter(UKF)algorithm was introduced to simulate the SOC of the lithium battery.The experimental results show that the UKF-based SOC estimation is more accurate,the error is smaller,and the convergence speed is faster,which is of great significance to the improvement of the traditional fixed-value battery parameter BMS.