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.
作者
吴忠强
陈海佳
WU Zhong-qiang;CHEN Hai-jia(Hebei Key Laboratory of Industrial Computer Control Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China)
出处
《计量学报》
CSCD
北大核心
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)。
