动态工况电池在线参数辨识及SOC估计研究

基于电池模型的荷电状态(SOC)估计方法,其估计精度主要取决于模型的精度。电池在动态工况下,输入电流变化激烈,传统的辨识方法因其收敛性差,导致模型精度降低。为了提高动态工况下电池模型精度,对传统带遗忘因子最小二乘法(FFRLS)进行改进,通过设置精度阈值,引入梯度矫正的方法,提出了改进带遗忘因子递推最小二乘法(IFFRLS)。利用改进算法进行在线参数辨识,建立二阶RC等效电路模型,与其他传统参数辨识建立的模型进行对比,验证IFFRLS对模型精度提高的有效性,模型平均误差为0.003 8 V。最后,将不同辨识方法所建立的模型与扩展卡尔曼滤波(EKF)算法进行联合估计SOC并对比其误差,结果表明通过IFFRLS辨识出来的高精度模型可有效提高SOC的估计精度,DST工况下,误差在1.51%以内。

Research on online parameter identification and SOC estimation of battery under dynamic conditions

The estimation accuracy of state of charge(SOC) based on battery model mainly depends on the accuracy of the model. Under dynamic conditions, the input current of the battery changes drastically, and the traditional identification method has poor convergence, which leads to the reduction of model accuracy. In order to improve the accuracy of battery model under dynamic conditions, the traditional least square method with forgetting factor(FFRLS) is improved. By setting the accuracy threshold and introducing gradient correction method, an improved recursive least square method with forgetting factor(IFFRLS) is proposed. Online parameter identification is carried out by using the improved algorithm, and a second-order RC equivalent circuit model is established. Compared with other models established by traditional parameter identification, the effectiveness of IFFRLS in improving the accuracy of the model is verified, and the average error of the model is 0.003 8 V. Finally, the models established by different identification methods are combined with EKF algorithm to estimate SOC, and their errors are compared. The results show that the high-precision model identified by IFFRLS can effectively improve the estimation accuracy of SOC, and the error is within 1.51% under DST condition.