基于自适应H_(2)/H_(∞)滤波的锂电池SOC和SOH联合估计  

Joint SOC and SOH Estimation for Lithium Batteries Based on Adaptive H_(2)/H_(∞)Filtering

作　　者：吴忠强[1] 陈海佳 WU Zhong-qiang;CHEN Hai-jia(Hebei Key Laboratory of Industrial Computer Control Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China)

机构地区：[1]燕山大学工业计算机控制工程河北省重点实验室,河北秦皇岛066004

出　　处：《计量学报》2023年第11期1719-1727,共9页Acta Metrologica Sinica

基　　金：河北省自然科学基金(F2020203014)。

摘　　要：准确、实时地估计电池的荷电状态(state of charge,SOC)和健康状态(state of health,SOH)是现代电池管理系统的关键任务。通过自适应H_(2)/H_(∞)滤波器可对锂电池的SOC和SOH进行联合估计。该方法基于锂电池的二阶RC等效电路模型,采用AFFRLS法在线辨识锂电池的模型参数,并利用H_(2)/H_(∞)滤波器估计锂电池的SOC,AFFRLS辨识与H_(2)/H_(∞)滤波交替进行,得到一种自适应H_(2)/H_(∞)滤波器。SOH依据AFFRLS辨识的电池内阻进行估计,实现了锂电池SOC与SOH的联合估计。实验结果表明:自适应H_(2)/H_(∞)滤波算法的估计精度高且鲁棒性强,电池的SOC和SOH的平均估计误差始终保持在±0.19%以内,相比于EKF和H_(∞)滤波算法有更高的估计精度与稳定性。Accurate and real-time estimation of a battery's state of charge(SOC)and state of health(SOH)is a key task of modern battery management systems.The SOC and SOH of lithium batteries can be estimated jointly by an adaptive H 2/H_(∞)filter.This method is based on the second-order RC equivalent circuit model of lithium battery,and AFFRLS method is used to identify the model parameters of lithium battery online.Using H_(2)/H_(∞)filter to estimate SOC of lithium battery,AFFRLS identification and H_(2)/H_(∞)filter are alternated to obtain an adaptive H_(2)/H_(∞)filter.SOH is estimated according to the internal resistance identified by AFFRLS,and the joint estimation of SOC and SOH of lithium battery is realized.The experimental results show that the adaptive H_(2)/H_(∞)filtering algorithm has high estimation accuracy and strong robustness,and the average estimation error of SOC and SOH of the battery is always within 0.19%,which has higher estimation accuracy and stability than EKF and H_(∞)filtering algorithm.

关 键 词：计量学 荷电状态 锂电池 健康状态 自适应H_(2)/H_(∞)滤波 参数辨识 联合估计 

分 类 号：TB971[一般工业技术—计量学]