| 引用本文: | 郭桂芳,曹秉刚.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[J].控制理论与应用,2011,28(4):591~595.[点击复制] |
| GUO Gui-fang,CAO Bing-gang.NARMAX method for estimating the residual capacity of Ni/MH battery pack for electric vehicle[J].Control Theory and Technology,2011,28(4):591~595.[点击复制] |
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| 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 2425 全文点击 2487 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). |
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| 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%. |