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A Novel On-line Parameter Identification and State of Charge Estimation of Lithium-ion Power Battery for Electric Vehicle

机译:电动汽车锂离子动力电池在线参数辨识与充电状态估计

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摘要

Accurated estimation of the battery state of charge is one of the key technologies in the research of hattery management system of electric vehicle.In order to estimate the state of charge of lithium-ion battery,we have chosen the Thevenin battery model as the lithium-ion battery equivalent circuit model.We established a state space model which had the advantage of simplicity and could be easily implemented based on the Thevenin battery model.And the Least Squares method is then applied to identificate the battery model parameters.Based on these,the model was linearizOed,and extended Kalman filter was applied in the estimation of the battery state of charge.Test and simulation results show that the Thevenin battery model can batter reflect the dynamic and static characteristics of the lithium-ion battery,and by one-line battery model parameters identification,the algorithm of extended Kalman filter can obtain better accuracy in the process of estimation of the state of charge.So the algorithm of extended Kalman filter has a strong application in the estimation of the state of charge.
机译:准确估计电池的充电状态是电动汽车孵化管理系统研究的关键技术之一。为了估计锂离子电池的充电状态,我们选择戴维南电池模型作为锂离子电池。离子电池等效电路模型。基于戴维南电池模型,我们建立了一个状态空间模型,该模型具有简单性并且易于实现。然后使用最小二乘方法识别电池模型参数。对模型进行了线性化,并采用扩展的卡尔曼滤波器对电池的充电状态进行了估计。测试和仿真结果表明,戴维南电池模型可以一线反映电池的动态和静态特性。电池模型参数识别,扩展卡尔曼滤波器算法在充电状态估计过程中可以获得更好的精度。扩展卡尔曼滤波器的算法在电荷状态的估计中具有强大的应用。

著录项

  • 来源
  • 会议地点 Changchun(CN)
  • 作者单位

    Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences;

    Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong;

    Shenzhen Key Laboratory of Electric Vehicle Powertrain Platform and Safety Technology;

    Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences;

    Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong;

    Shenzhen Key Laboratory of Electric Vehicle Powertrain Platform and Safety Technology;

    Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences;

    Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong;

    Department of Electrical Engineering,Tongji University;

    Shenzhen Key Laboratory of Electric Vehicle Powertrain Platform and Safety Technology;

    Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences;

    Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong;

    Shenzhen Key Laboratory of Electric Vehicle Powertrain Platform and Safety Technology;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 汽车工程;
  • 关键词

    extended kalman filter; thevenin battery model; state of charge(SOC) estimation; lithium-ion battery; electric vehicles;

    机译:扩展卡尔曼滤波器;戴维南电池模型;荷电状态估计;锂离子电池;电动汽车;

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