锂电池自适应无迹H∞滤波SOC估计研究  

State of charge estimation of lithium batteries using adaptive unscented H infinity filter

作　　者：钱伟[1,2] 赵大中 郭向伟 王亚丰 李文静 QIAN Wei;ZHAO Dazhong;GUO Xiangwei;WANG Yafeng;LI Wenjing(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,Henan,China;Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment,Jiaozuo 454003,Henan,China)

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454003 [2]河南省煤矿装备智能检测与控制重点实验室,河南焦作454003

出　　处：《储能科学与技术》2024年第11期4078-4088,共11页Energy Storage Science and Technology

基　　金：国家自然科学基金面上项目(62373137);河南省重点研发专项(241111241700)。

摘　　要：荷电状态(state of charge,SOC)作为表征锂电池剩余电量的关键指标,其精确估计对于合理使用电池电量、保障电池安全具有重要意义。本文针对基于H∞滤波(H infinity filter,HIF)估计SOC时鲁棒性好但估计精度低的问题,提出一种自适应无迹H∞滤波(adaptive unscented H infinity filter,AU_HIF)SOC估计方法,以提高SOC估计精度。首先,选择能够在精度和复杂度间取得良好平衡的双极化(dual polarization,DP)等效电路模型进行新型估计算法的设计;其次,结合无迹卡尔曼滤波(unscented Kalman filter,UKF)算法相比于传统扩展卡尔曼滤波(extended Kalman filter,EKF)算法更适用于非线性系统状态估计的特点,文章基于先验误差协方差矩阵设计新型渐消因子,实现自适应无迹H∞滤波算法的设计,以减小陈旧测量值对估计结果的影响,提高滤波算法的跟踪能力及估计精度。最后,通过搭建自主实验平台获取实际模拟工况数据,验证了文章所提自适应无迹H∞滤波算法相比于传统H∞滤波算法、传统UKF算法和其他类型改进H∞滤波算法具有更高的估计精度及更好的鲁棒性。文章研究内容对提高新能源汽车、储能电站等电池系统的SOC估计精度具有重要意义。The state of charge(SOC)is a crucial metric for assessing the remaining power of lithium batteries,playing a significant role in optimizing battery usage and ensuring safety.To address the challenge of SOC estimation using the H infinity frilter(HIF),which offers high robustness but limited accuracy,this study proposes an adaptive unscented H infinity filter(AU_HIF)to enhance estimation precision.The dual polarization equivalent circuit model,known for its balanced accuracy and complexity,is selected to develop the new estimation algorithm.The unscented Kalman filter(UKF),which is more suitable for nonlinear state estimation compared to the traditional extended Kalman filter,is combined with a novel fading factor designed based on the prior error covariance matrix.This design minimizes the impact of outdated measurements on estimation results,improving the tracking capability and accuracy of the filtering algorithm.The effectiveness of the proposed AU_HIF is validated through simulations using data collected from a custom-built experimental platform.Results demonstrate that the adaptive unscented HIF outperforms traditional H infinity filtering,the standard UKF,and other modified H infinity filtering algorithms in terms of estimation accuracy and robustness.This research significantly enhances SOC estimation for battery systems used in new energy vehicles and energy storage power stations.

关 键 词：锂电池 SOC H∞滤波 DP模型 渐消因子 

分 类 号：TM912.8[电气工程—电力电子与电力传动]