基于改进FFRLS算法的锂离子电池参数辨识  

Parameter identification of Li-ion battery based on improved FFRLS algorithm

作　　者：刘晓静 李建良[1] 南忠良[1] 郭秋蕊 LIU Xiaojing;LI Jianliang;NAN Zhongliang;GUO Qiurui(School of Electronic Information and Automation,Tianjin University of Science and Technology,Tianjin 300222,China)

机构地区：[1]天津科技大学电子信息与自动化学院,天津300222

出　　处：《电源技术》2022年第6期657-660,共4页Chinese Journal of Power Sources

摘　　要：动力电池荷电状态的估算在电池管理系统中十分重要,电池模型精确度和参数辨识准确度对其有决定性影响。以磷酸铁锂电池为研究对象,选用一阶RC电池模型,对时变参数进行辨识。传统的带遗忘因子最小二乘法在参数辨识过程中,电压误差值存在偏差,导致模型精度降低。为了提高动态工况下的模型精度,引入比例控制算法对电压误差进行修正。结果表明,在城市道路循环工况(UDDS)下,改进的带遗忘因子最小二乘算法的误差在0.03 V以内,提高了系统参数辨识的准确度和电池模型精确度。The state of charge estimation of power battery is very important in battery management system,and the accuracy of battery model and parameter identification has a decisive influence on it.With lithium iron phosphate battery as study object,the first-order RC battery model was selected to identify the time-varying parameters.In the parameter identification process of the traditional least square method with forgetting factor,the voltage error value has deviation,which leads to the decrease of model accuracy.In order to improve the model accuracy under dynamic conditions,the proportional control algorithm was introduced to compensate the voltage error.The results show that the error of the improved least square algorithm with forgetting factor is less than 0.03 V,which improves the accuracy of system parameter identification and battery model under urban dynamometer driving schedule(UDDS).

关 键 词：电池模型 比例控制 参数辨识 遗忘因子 最小二乘法 

分 类 号：TM912.9[电气工程—电力电子与电力传动]