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Modeling and state of charge estimation of Lion Battery using the autoregressive exogenous model
编号：85 访问权限：仅限参会人 更新：2020-10-28 15:22:45 浏览：331次 口头报告

报告开始：2020年11月03日 09:15（Asia/Shanghai）

报告时间：15min

所在会场：[A] Power System [A3] Session 11 and Session 16

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摘要
      State of charge (SOC) estimation of lithium-ion batteries is the key technology of battery management system. In this paper, a method for estimating SOC of lithium-ion batteries based on ARX-AKF algorithm is proposed. The lithium-ion battery model adopts the autoregressive exogenous (ARX) model. The model order is determined by the genetic algorithm based on the Akaike's information criterion (AIC) and the model parameters are obtained by the recursive least squares, thereby solving the problem which is difficult to obtain the parameters of the equivalent circuit model accurately. Secondly, the adaptive Kalman filter (AKF) algorithm is used to estimate the SOC of the lithium-ion batteries based on the established ARX model. Finally, the performance of the algorithm is verified by the hybrid pulse power characteristic (HPPC) experiment. The experimental results show that the algorithm proposed in this paper has advantages of high precision, fast convergence, low computation cost and good practical value.
关键词
ARX-AKF algorithm, lithium-ion battery, state of charge
报告人
Xue Jiang
Northwestern Polytechnical University

稿件作者
Xue Jiang Northwestern Polytechnical University
Bowen Zhang Northwestern Polytechnical University
Yi Xiang Northwestern Polytechnical University
Yufeng Wang Northwestern Polytechnical University
Weilin Li Northwestern Polytechnical University
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重要日期

  • 会议日期

    11月02日

    2020

    至

    11月04日

    2020

  • 10月27日 2020

    初稿截稿日期

  • 11月03日 2020

    报告提交截止日期

  • 11月04日 2020

    注册截止日期

  • 11月17日 2020

    终稿截稿日期

主办单位
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
承办单位
Huazhong University of Science and Technology
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