上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (12): 1935-1945.doi: 10.16183/j.cnki.jsjtu.2023.168

• 新型电力系统与综合能源 • 上一篇    下一篇

考虑驾驶工况及老化程度的锂电池荷电状态估算

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   

  1. 1.西安工程大学 电子信息学院,西安 710048
    2.西安市电气设备互联感知与智能诊断重点实验室,西安 710048
    3.国电南瑞科技股份有限公司,南京 211106
  • 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:
    陕西省科技厅自然科学基础研究重点项目(2022JZ-35)

State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree

WU Jiang1,2(), ZHANG Yan1,2, LIU Zelong1,2, CHENG Gang1,2, LEI Dong1,2, JIAO Chaoyong3   

  1. 1. School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China
    2. Xi’an Key Laboratory of Interconnected Sensing and Intelligent Diagnosis for Electrical Equipment, Xi’an 710048, China
    3. NARI Technology Co., Ltd., Nanjing 211106, China
  • 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.

关键词: 锂离子电池, 荷电状态, 复杂工况, 电池老化, 自适应双扩展卡尔曼滤波

Abstract:

When using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of an electric vehicle power battery, the change of system noise and model parameters may lead to a reduction in estimation accuracy, due to variable operating conditions, battery aging, and other factors. The NCR18650B ternary lithium-ion battery is selected, and the second-order RC model is established with identified parameters. Then, by using EKF as the main body with a fixed measurement noise covariance and adaptively adjusting process noise covariance based on the maximum likelihood estimation criterion, an adaptive extended Kalman filter is built to estimate the SOC of the battery. Simultaneously, a Kalman filter is used to estimate the ohmic resistance in real time. Thus, an adaptive dual extended Kalman filter (ADEKF) algorithm is formed. Finally, algorithm verifications are performed with testing data and public datasets. The ADEKF proposed is used to estimate the SOC of five groups of aged lithium batteries under three operating conditions, which are constant current, dynamic stress test, and Beijing dynamic stress test, and compared with that of EKF and other algorithms. The results show that compared with EKF, the average absolute error of the estimation results of ADEKF for different aged batteries under three operating conditions decreases by 1.868 percentage points, 2.296 percentage points, and 2.534 percentage points, respectively, which proves that ADEKF algorithm can effectively improve the SOC estimation accuracy under multiple operating conditions, battery aging and the combination of the two factors.

Key words: lithium-ion battery, state of charge(SOC), complex operating conditions, battery aging, adaptive dual extended Kalman filter (ADEKF)

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