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Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach

机译：基于改进的无需卡尔曼滤波方法的锂离子电池在线估算在线状态

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摘要

An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to address the drawback associated with the unscented Kalman filter in terms of its requirement for an accurate model and a priori noise statistics. Firstly, Li-ion battery modelling and offline parameter identification is performed. Secondly, a sensitivity analysis experiment is designed to verify which model parameter has the greatest influence on state of charge estimation accuracy, in order to provide an appropriate parameter for the model adaptive algorithm. Thirdly, an improved unscented Kalman filter approach, composed of a model adaptive algorithm and a noise adaptive algorithm, is introduced. Finally, the results are discussed, which reveal that the proposed approach's estimation error is less than 1.79% with acceptable robustness and time complexity. (C) 2019 Elsevier Inc. All rights reserved.
机译：提出了一种改进的无创的卡尔曼滤波方法，以提高精度和鲁棒性方面的在线充电估计状态。目的是在其要求准确模型和先验噪声统计数据方面解决与Unscented Kalman滤波器相关联的缺点。首先，执行锂离子电池建模和离线参数识别。其次，敏感性分析实验旨在验证哪个型号参数对电荷估计准确性最大的影响最大，以便为模型自适应算法提供适当的参数。第三，引入了由模型自适应算法和噪声自适应算法组成的改进的无创的卡尔曼滤波器方法。最后，讨论了结果，揭示了所提出的方法的估计误差小于1.79％，具有可接受的稳健性和时间复杂性。 （c）2019 Elsevier Inc.保留所有权利。

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  • 来源
    《Applied Mathematical Modelling》 |2019年第6期|532-544|共13页
  • 作者

    Chen Zewang; Yang Liwen; Zhao Xiaobing; Wang Youren; He Zhijia;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut Coll Automat & Engn Nanjing 211106 Jiangsu Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Automat & Engn Nanjing 211106 Jiangsu Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Automat & Engn Nanjing 211106 Jiangsu Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Automat & Engn Nanjing 211106 Jiangsu Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Automat & Engn Nanjing 211106 Jiangsu Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Li-ion battery; State of charge estimation; Unscented Kalman filter; Model adaptive; Noise adaptive;

    机译：锂离子电池;充电状态估计;无味的卡尔曼滤波器;模型自适应;噪声适应;

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