基于H∞无迹卡尔曼滤波的退役锂离子电池SOC估计

TM912; 电动汽车的锂离子动力电池退役后,具备在储能系统等场合继续使用的潜力,其SOC(电池荷电状态)的准确估计对于退役电池的梯次利用具有重要意义.针对传统UKF(无迹卡尔曼滤波)算法出现模型参数不确定及采样噪声干扰导致估算精度下降甚至系统发散等问题,提出一种HUKF(H∞无迹卡尔曼滤波)算法.该算法在UKF基础上,利用H∞控制理论引入调整因子来修正UKF中计算协方差时遇到的病态矩阵,提高对异常值和非高斯噪声的鲁棒性.实验结果表明,改进算法以较快的收敛速度实现了更精确的SOC估计,且鲁棒性较好,满足了退役电池SOC估计的实际需求....

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Published in浙江电力 Vol. 39; no. 8; pp. 53 - 60
Main Authors 谢宝江, 娄伟明, 罗扬帆, 王华昕, 李珂
Format Journal Article
LanguageChinese
Published 国网浙江省电力有限公司台州供电公司,浙江 台州 318000%上海电力大学,上海 200090 2020
Subjects
H∞控制
荷电状态
无迹卡尔曼滤波
调整因子
退役电池
Online AccessGet full text
ISSN1007-1881
DOI10.19585/j.zjdl.202008009

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Abstract TM912; 电动汽车的锂离子动力电池退役后,具备在储能系统等场合继续使用的潜力,其SOC(电池荷电状态)的准确估计对于退役电池的梯次利用具有重要意义.针对传统UKF(无迹卡尔曼滤波)算法出现模型参数不确定及采样噪声干扰导致估算精度下降甚至系统发散等问题,提出一种HUKF(H∞无迹卡尔曼滤波)算法.该算法在UKF基础上,利用H∞控制理论引入调整因子来修正UKF中计算协方差时遇到的病态矩阵,提高对异常值和非高斯噪声的鲁棒性.实验结果表明,改进算法以较快的收敛速度实现了更精确的SOC估计,且鲁棒性较好,满足了退役电池SOC估计的实际需求.
AbstractList TM912; 电动汽车的锂离子动力电池退役后,具备在储能系统等场合继续使用的潜力,其SOC(电池荷电状态)的准确估计对于退役电池的梯次利用具有重要意义.针对传统UKF(无迹卡尔曼滤波)算法出现模型参数不确定及采样噪声干扰导致估算精度下降甚至系统发散等问题,提出一种HUKF(H∞无迹卡尔曼滤波)算法.该算法在UKF基础上,利用H∞控制理论引入调整因子来修正UKF中计算协方差时遇到的病态矩阵,提高对异常值和非高斯噪声的鲁棒性.实验结果表明,改进算法以较快的收敛速度实现了更精确的SOC估计,且鲁棒性较好,满足了退役电池SOC估计的实际需求.
Author 娄伟明
李珂
罗扬帆
谢宝江
王华昕
AuthorAffiliation 国网浙江省电力有限公司台州供电公司,浙江 台州 318000%上海电力大学,上海 200090
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Author_FL WANG Huaxin
LUO Yangfan
XIE Baojiang
LOU Weiming
LI Ke
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荷电状态
无迹卡尔曼滤波
调整因子
退役电池
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Title 基于H∞无迹卡尔曼滤波的退役锂离子电池SOC估计
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