MIAEKF算法对锂电池荷电状态估算的研究

Research on MIAEKF algorithm for estimating lithium battery SOC

为弥补扩展卡尔曼滤波算法估算锂离子动力电池的荷电状态(SOC)时误差大的缺点,从而更加有效地监测电池的状态,文中以二阶RC等效电路模型为基础,建立数学关系简单、易于工程实现的状态空间模型。在递推最小二乘法的基础上加入自适应因子来辨识二阶电路模型中相应的参数,并进行电路模型精确度验证;然后,结合多创新的自适应扩展卡尔曼滤波算法(MIAEKF)对电池荷电状态(SOC)进行精准估算;最后,利用Matlab数值软件编程该算法并进行仿真验证。仿真结果表明,基于多创新的自适应扩展卡尔曼滤波算法估算电池SOC的平均误差最小为1.12%,估算的最大误差为2.69%,说明基于多创新的自适应扩展卡尔曼滤波算法在估算过程中有更高的精度和更快的收敛速度,对锂离子电池荷电状态的精度有较精准的估计。

In order to compensate defect of the large error caused by the extended Kalman filter algorithm in estimating the state of charge(SOC)of lithium⁃ion power batteries and more effectively monitor the state of the battery,a state⁃space model with simple mathematical relationships and being easy to implement in engineering is established by taking second⁃order RC equivalent circuit model as the basics.On the basis of the recursive least square method,an adaptive factor is added to identify the corresponding parameters in the second⁃order circuit model and verify the accuracy of the circuit model.The multi⁃innovative adaptive extended Kalman filter(MIAEKF)algorithm is used to accurately estimate SOC of the battery,and the Matlab numerical software is used to program the algorithm and perform the simulation verification.The simulation results show that the minimum average error of the battery SOC estimated on the basis of MIAEKF algorithm is 1.12%,and the maximum error estimated by the algorithm is 2.69%.It can be seen that the MIAEKF algorithm has higher accuracy and faster convergence speed in the process of estimation,and can better estimate the accuracy of the lithium⁃ion battery SOC.