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汽车安全与节能学报

汽车安全与节能学报 ›› 2019, Vol. 10 ›› Issue (3): 342-348.DOI: 10.3969/j.issn.1674-8484.2019.03.010

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影响锂离子电池荷电状态估算精度的因素分析

张  茁1,2，刘建宇 1,2，王璐平 1,2，金  鹏 1,2*#br#   

  1. （1. 北方工业大学 电气与控制工程学院，北京 100144，中国； 2. 北京电动汽车协同创新中心，北京 100144，中国）
  • 收稿日期:2019-12-26 出版日期:2019-09-30 发布日期:2019-10-01
  • 通讯作者: 金鹏 (1980—），男( 汉），黑龙江，讲师。E-mail: jpzy216@163.com。
  • 作者简介:张茁(1995—）, 男(汉)，北京，硕士研究生。E-mail: 1519862047@qq.com。

Analysis on the factors of affecting the estimation accuracy of lithium battery's state of charge

ZHANG Zhuo 1,2, LIU Jianyu 1,2, WANG Luping 1,2, JIN Peng 1,2*   

  1. （1．College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China;  2. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100144, China)
  • Received:2019-12-26 Online:2019-09-30 Published:2019-10-01

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摘要：

       为了考察引起荷电状态（SOC）估算误差各因素的不同作用，以3块磷酸铁锂电池组成的电池 组为对象进行混合脉冲功率特性（HPPC）实验，二阶Thevenin电池模型为基础构建状态方程，利用 无迹Kalman滤波（UKF）算法估算SOC，分析模型参数及输入电流、端电压、开路电压（OCV）误 差对SOC估算的影响。结果表明：当在OCV、端电压及输入电流中各加入1%的噪声，或在电池的 参数（欧姆内阻，极化电阻，极化电容）固定时，SOC估计的误差分别为19.1%、20.6%和小于3%； 可见OCV和端电压对SOC估算精度影响较大，提高端电压的测量精度和OCV曲线拟合的准确性， 可大幅减小锂电池SOC的估算误差。

关键词: 电池模型 , 无迹Kaltman滤波 , 荷电状态(SOC)估算 , 开路电压

Abstract:

A hybrid pulse power characteristic (HPPC) experiment was carried out on a battery pack composed of three lithium iron phosphate batteries to investigate the different effects of various factors causing the state of charge (SOC) estimation error. A second-order Thevenin battery model was used to construct a state equation, and SOC was estimated using the unscented Kalman filter (UKF) algorithm, and the influences of the errors of the model parameters, input current, terminal voltage, and open circuit voltage (OCV) on SOC estimation were analyzed. The results show that the SOC estimation error is 19.1%, 20.6% and less than 3%, when 1% noise is added to the OCV, terminal voltage, and input current, or when the parameters of the battery (ohm internal resistance, polarization resistance, polarization capacitance) are fixed, respectively, showing that the OCV and the terminal voltage have a great influence on the SOC estimation accuracy. The estimation error of the SOC of the lithium battery can be greatly reduced by the improvement of the measurement accuracy of the terminal voltage and the accuracy of the OCV curve fitting.

Key words:  battery model , unscented Kalman filter ,  estimation of state of charge (SOC) ,  open circuit voltage