Daniil Fadeev1， ZHANG Xiao-zhou2， DONG Hai-ying1， LIU Hao2， ZHANG Rui-ping1
（1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China； 2. State Key Laboratory of Large Electric Transmission Systems and Equipment Technology, Tianshui Electric Drive Research Institute Group Co., Ltd., Tianshui 741020, China）
Abstract： This paper presents a more accurate battery state of charge (SOC) and state of health (SOH) estimation method. A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model. The model parameters are estimated by searching least square error optimization algorithm. Precisely defined by this method, the model parameters allow to accurately determine the capacity of the battery, which in turn allows to specify the SOC prediction value used as a basis for the SOH value. Application of the extended Kalman filter (EKF) removes the need of prior known initial SOC, and applying the fuzzy logic helps to eliminate the measurement and process noise. Simulation results obtained during the urban dynamometer driving schedule (UDDS) test show that the maximum error in estimation of the battery SOC is 0.66%. Battery capacity is estimate by offline updated Kalman filter, and then SOH will be predicted. The maximum error in estimation of the battery capacity is 1.55%.
Key words： lithium battery; state of charge (SOC); state of health (SOH); adaptive extended Kalman filter (AEKF)
CLD number： TM911 doi： 10.3969/j.issn.1674-8042.2020.01.008
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基于模糊卡尔曼滤波器的锂电池荷电状态 与健康状态预测
Daniil Fadeev1， 张小周2， 董海鹰1， 刘 浩2， 张蕊萍1
（1. 兰州交通大学自动化与电气工程学院， 甘肃 兰州 730070； 2. 天水电气传动研究所有限责任公司 大型电气传动系统与装备技术国家重点实验室， 甘肃 天水 741020）
摘 要：针对当前锂电池荷电状态（State of charge, SOC）与健康状态（State of health, SOH）预测精度较低的问题， 提出了一种基于模糊卡尔曼滤波器的预测方法。 采用非线性二阶电阻电容模型表示锂电池， 并通过最小二乘误差优化算法对模型参数进行估计， 从而更准确地确定蓄电池容量作为SOH值的基础。 扩展卡尔曼滤波器（Extended Kalman filter, EKF）可在初始SOC值未知的情况下对其进行准确预测， 而模糊逻辑有助于消除测量和过程噪声。 仿真结果表明， 在城市测功机驱动计划期间（Urban dynamometer drving schedule, UDDS）测试中最大的SOC估算误差是0.66%； 通过离线更新卡尔曼滤波器， 可对电池容量进行估计， 结果表明， 最大估计误差为1.55%， 从而有效提高了SOC值的预测精度。
关键词： 锂电池； 荷电状态； 健康状态； 自适应扩展卡尔曼滤波器
引用格式： Daniil Fadeev， ZHANG Xiao-zhou， DONG Hai-ying, et al. Lithium battery state of charge and state of health prediction based on fuzzy Kalman filtering. Journal of Measurement Science and Instrumentation, 2020, 11（1）： 63-69. ［doi： 10.3969／j.issn.1674-8042.2020.01.008］
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