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首页> 外文期刊>Mathematical Problems in Engineering >Comparison of Nonlinear Filtering Methods for Estimating the State of Charge of Li4Ti5O12 Lithium-Ion Battery
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Comparison of Nonlinear Filtering Methods for Estimating the State of Charge of Li4Ti5O12 Lithium-Ion Battery

机译：估算Li4Ti5O12锂离子电池充电状态的非线性滤波方法比较

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摘要

Accurate state of charge (SoC) estimation is of great significance for the lithium-ion battery to ensure its safety operation and to prevent it from overcharging or overdischarging. To achieve reliable SoC estimation for Li4Ti5O12 lithium-ion battery cell, three filtering methods have been compared and evaluated. A main contribution of this study is that a general three-step model-based battery SoC estimation scheme has been proposed. It includes the processes of battery data measurement, parametric modeling, and model-based SoC estimation. With the proposed general scheme, multiple types of model-based SoC estimators have been developed and evaluated for battery management system application. The detailed comparisons on three advanced adaptive filter techniques, which include extend Kalman filter, unscented Kalman filter, and adaptive extend Kalman filter (AEKF), have been implemented with a Li4Ti5O12 lithium-ion battery. The experimental results indicate that the proposed model-based SoC estimation approach with AEKF algorithm, which uses the covariance matching technique, performs well with good accuracy and robustness; the mean absolute error of the SoC estimation is within 1% especially with big SoC initial error.
机译：准确的充电状态（SoC）估计对于锂离子电池确保其安全运行并防止其过度充电或过度放电具有重要意义。为了对Li4Ti5O12锂离子电池实现可靠的SoC估计，已对三种滤波方法进行了比较和评估。这项研究的主要贡献在于，提出了一种基于三步模型的通用电池SoC估计方案。它包括电池数据测量，参数建模和基于模型的SoC估计的过程。通过提出的通用方案，已开发出多种类型的基于模型的SoC估计器，并对其进行了评估，以用于电池管理系统应用。已使用Li4Ti5O12锂离子电池对三种先进的自适应滤波器技术进行了详细的比较，其中包括扩展卡尔曼滤波器，无味卡尔曼滤波器和自适应扩展卡尔曼滤波器（AEKF）。实验结果表明，基于协方差匹配技术的基于模型的基于AEKF算法的SoC估计方法具有良好的精度和鲁棒性。 SoC估计的平均绝对误差在1％以内，特别是在SoC初始误差较大的情况下。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第20期|485216.1-485216.12|共12页
  • 作者

    Gao Jianping; He Hongwen;

  • 作者单位

    Henan Univ Sci & Technol, Coll Vehicle & Transportat Engn, Luoyang 471023, Peoples R China;

    Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China;

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