基于自适应H2/H&infin;滤波的锂电池SOC和SOH联合估计

吴忠强; 陈海佳

doi:10.3969/j.issn.1000-1158.2023.11.13

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计量学报 ›› 2023, Vol. 44 ›› Issue (11) : 1719-1727. DOI: 10.3969/j.issn.1000-1158.2023.11.13

电磁学计量

基于自适应H2/H∞滤波的锂电池SOC和SOH联合估计

吴忠强,陈海佳

作者信息 +

Joint SOC and SOH Estimation for Lithium Batteries Based on Adaptive H2/H∞ Filtering

WU Zhong-qiang,CHEN Hai-jia

Author information +

文章历史 +

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H2/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H2/H∞滤波器估计锂电池的SOC,AFFRLS辨识与H2/H∞滤波交替进行,得到一种自适应H2/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H2/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。

Abstract

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 H2/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 H2/H∞ filter to estimate SOC of lithium battery, AFFRLS identification and H2/H∞ filter are alternated to obtain an adaptive H2/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 H2/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.

导出引用

吴忠强,陈海佳. 基于自适应H2/H∞滤波的锂电池SOC和SOH联合估计[J]. 计量学报. 2023, 44(11): 1719-1727 https://doi.org/10.3969/j.issn.1000-1158.2023.11.13

WU Zhong-qiang,CHEN Hai-jia. Joint SOC and SOH Estimation for Lithium Batteries Based on Adaptive H2/H∞ Filtering[J]. Acta Metrologica Sinica. 2023, 44(11): 1719-1727 https://doi.org/10.3969/j.issn.1000-1158.2023.11.13

中图分类号： TB971

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