基于IMAFFRLS-EKF的锂电池在线参数辨识和SOC估计方法

ONLINE PARAMETER IDENTIFICATION AND SOC ESTIMATION OFLITHIUM BATTERY BASED ON IMAFFRLS-EKF

针对基于扩展卡尔曼滤波(EKF)法锂离子电池SOC估计,易受最小二乘法及其改进方法的模型参数在线辨识精度影响,提出一种改进遗忘因子的最小二乘在线参数辨识方法(IMAFFRLS)。以双极化等效电路模型为基础,分析传统的基于遗忘因子的最小二乘法(FFRLS)辨识模型参数时产生误差的原因,指出单一遗忘因子难以准确跟踪多个以不同速率变化的模型参数。通过对FFRLS算法中的协方差和增益矩阵解耦,引入多个可变遗忘因子独立修正不同参数的估计误差;并以移动区间内的输入电流波动程度和输出电压观测误差为依据,实现各遗忘因子的自适应变化。此外,将改进前后的两种参数辨识算法分别与EKF算法联合,实现锂离子电池SOC估计。最后基于Matlab进行对比仿真验证,结果表明,相对于FFRLS-EKF算法,所提出的IMAFFRLS-EKF算法辨识模型参数以及估计SOC的精度更高。

A least-squares online parameter identification method with an improved forgetting factor(IMAFFRLS)is proposed for the extended Kalman filter(EKF)method-based lithium-ion battery SOC estimation,which is vulnerable to the online identification accuracy of model parameters by the least-squares method and its improvement methods.Based on the dual-polarized equivalent circuit model,the causes of errors in the traditional forgetting factor-based least squares(FFRLS)method for identifying model parameters are analyzed,and it is noted that it is difficult to accurately track multiple model parameters varying at different rates with a single forgetting factor.By decoupling the covariance and gain matrices in the FFRLS algorithm,multiple variable forgetting factors are introduced to independently correct the estimation errors of different parameters.The adaptive variation of each forgetting factor is achieved based on the degree of input current fluctuation and the output voltage observation error in the moving interval.In addition,the two parameter identification algorithms before and after the improvement are combined with the EKF algorithm to realize the SOC estimation of Li-ion batteries.Finally,a comparative simulation based on Matlab is performed to verify the proposed model.The simulation results show that the proposed IMAFFRLS-EKF algorithm can identify the model parameters and estimate the SOC with higher accuracy than the FFRLSEKF algorithm.