上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (12): 1935-1945.doi: 10.16183/j.cnki.jsjtu.2023.168
乌江1,2(), 张燕1,2, 刘泽龙1,2, 程刚1,2, 雷冬1,2, 焦朝勇3
收稿日期:
2023-05-04
修回日期:
2023-06-13
接受日期:
2023-06-16
出版日期:
2024-12-28
发布日期:
2025-01-06
作者简介:
乌 江(1986—),副教授,主要从事电池参数估计研究;E-mail: wujiang@xpu.edu.cn.
基金资助:
WU Jiang1,2(), ZHANG Yan1,2, LIU Zelong1,2, CHENG Gang1,2, LEI Dong1,2, JIAO Chaoyong3
Received:
2023-05-04
Revised:
2023-06-13
Accepted:
2023-06-16
Online:
2024-12-28
Published:
2025-01-06
摘要:
扩展卡尔曼滤波(EKF)估算电动汽车动力电池荷电状态(SOC)时,驾驶工况随机多变、电池老化等因素造成系统噪声与模型参数变化,导致估算精度降低.以NCR18650B型三元锂离子电池为研究对象,首先采用二阶RC等效电路模型并对其模型参数进行辨识;其次,以EKF算法为主体,基于极大似然估计准则,固定测量噪声协方差并自适应调整过程噪声协方差,构建自适应扩展卡尔曼滤波算法估算电池SOC;同时,应用卡尔曼滤波算法实时估算等效电路模型中欧姆内阻,最终结合形成自适应双扩展卡尔曼滤波算法(ADEKF).最后,利用自有实验数据和公开数据集验证算法性能.利用所提ADEKF算法估算恒流工况、动态应力工况与北京公交车动态应力测试工况下5组不同老化程度锂离子电池SOC,并与EKF等常见算法进行对比分析.结果表明:相比EKF算法,所提ADEKF算法对不同老化电池在3种工况下,估算结果的平均绝对误差分别下降1.868百分点、2.296百分点和2.534百分点,证明该算法有效提高在动力工况、电池老化以及二者综合因素下的锂离子电池SOC估算精度.
中图分类号:
乌江, 张燕, 刘泽龙, 程刚, 雷冬, 焦朝勇. 考虑驾驶工况及老化程度的锂电池荷电状态估算[J]. 上海交通大学学报, 2024, 58(12): 1935-1945.
WU Jiang, ZHANG Yan, LIU Zelong, CHENG Gang, LEI Dong, JIAO Chaoyong. State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree[J]. Journal of Shanghai Jiao Tong University, 2024, 58(12): 1935-1945.
表5
典型工况下ADEKF算法估算老化电池ξSOC的误差对比
SOH | 恒流 | DST | BBDST | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
δMAE/% | δME/% | 收敛时间/s | δMAE/% | δME/% | 收敛时间/s | δMAE/% | δME/% | 收敛时间/s | |||
100% | 0.46 | 1.96 | 130 | 0.41 | 2.64 | 56 | 0.38 | 2.33 | 158 | ||
94.9% | 0.47 | 2.41 | 490 | 0.63 | 4.55 | 56 | 0.47 | 6.34 | 54 | ||
90.8% | 0.56 | 2.08 | 530 | 1.20 | 1.84 | 76 | 1.02 | 3.89 | 55 | ||
84.6% | 0.62 | 1.97 | 445 | 2.45 | 2.85 | 76 | 1.58 | 2.06 | 110 | ||
79.2% | 0.66 | 2.99 | 510 | 1.89 | 4.28 | 56 | 2.20 | 2.62 | 111 | ||
EKF最小误差 | 1.94 | 2.50 | 2.74 | ||||||||
AEKF最小误差 | 1.17 | 2.06 | 2.29 |
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