基于VFFRLS算法的锂电池参数辨识

动力电池性能是影响电动汽车综合性能的关键因素，因此准确辨识锂离子电池模型的参数对后续电池系统的荷电状态估计和健康状态预测至关重要。为了提高锂离子电池模型参数辨识算法的精度，以磷酸铁锂电池作为研究对象，建立电池二阶RC等效电路模型，并采用基于变量遗忘因子的最小二乘算法对锂离子电池模型进行在线参数辨识。通过搭建测试平台进行充放电实验，基于2种不同工况的实验数据，分别用文中算法、递推最小二乘算法和传统的带遗忘因子的最小二乘算法进行参数辨识，根据辨识结果估计出的端口电压与实验测试得到的实际值的误差比较来描述文中算法辨识结果的准确度。实验结果表明，基于变量遗忘因子的最小二乘算法在锂电池参数辨识方面表现出快速的收敛性和较高的估计精度。

Parameter identification of lithium-ion battery based on least squares algorithm with variable forgetting factor

Power battery performance plays a pivotal role in the comprehensive performance of electric vehicles,and thus accurate identification of the parameters of the lithium-ion battery model is crucial for subsequent state-of-charge estimation and state-of-health prediction of the battery system. In order to improve the accuracy of parameter identification algorithm of lithium-ion battery model,a second-order RC equivalent circuit model of the battery is established,and a least-squares algorithm based on variable forgetting factor is used to identify the parameters of the lithium-ion battery model online. By building a test platform for charge and discharge experiments,based on the experimental data of two different operating conditions,the proposed algorithm,recursive least squares algorithm and traditional forgetting factor least squares algorithm are used to identify the parameters,and the accuracy of the proposed algorithm is described based on the comparison of the error between the estimated port voltage and the actual value obtained from the experimental test. The experimental results show that the recursive least squares algorithm based on the variable forgetting factor shows fast convergence and high estimation accuracy in the identification of lithium-ion battery parameters.