电动车用Ni/MH电池组剩余容量的非线性自回归滑动平均预测
NARMAX method for estimating the residual capacity of Ni/MH battery pack for electric vehicle
摘要点击 1359  全文点击 2034  投稿时间:2009-09-11  修订日期:2010-04-29
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DOI编号  10.7641/j.issn.1000-8152.2011.4.CCTA091174
  2011,28(4):591-595
中文关键词  电动汽车  Ni/MH电池组  荷电状态  NARMAX  辨识预测
英文关键词  electric vehicle  Ni/MH battery pack  state of charge(SOC)  NARMAX method  prediction
基金项目  中国科技部星火计划资助项目(2006EA105003).
作者单位E-mail
郭桂芳 西藏民族学院 信息工程学院
西安交通大学 机械工程学院 
ggf8053@163.com 
曹秉刚 西安交通大学 机械工程学院  
中文摘要
      准确的蓄电池荷电状态(SOC)决定了电动汽车剩余的行驶里程数.为准确评估电动车用Ni/MH电池组荷电状态(SOC)值, 本文提出了一种非线性自回归滑动平均(NARMAX)模型的系统辨识方法.文中使用联邦城市行驶工况(FUDS)的试验数据, 采用NARMAX模型线性简化逼近的辨识方法, 对蓄电池SOC建立了多输入变量的模型, 并使用这个模型进行实时预测; 预测结果与试验结果进行了比较. 结果表明, 该方法是简单、有效的. 预测的最大相对误差为1%.
英文摘要
      An accurate state of charge(SOC) determines the residual diving distance of electric vehicles. For evaluating the state of charge(SOC) of the Ni/MH battery pack for electric vehicle, we propose an identification approach using NARMAX(nonlinear auto-regressive moving average with exogenous inputs) model. Employing the federal urban driving schedule(FUDS) tested data and adopting the simplified linear approximation of NARMAX method, we build the multiinput model for the SOC of the battery pack. This model is used for predicting the real-time SOC, and the results are compared with the tested data. The comparison of the predicted results with the tested data shows that the proposed method is simple and efficient. The maximum relative error of the estimation results is within 1%.