基于在线参数辨识和AEKF的锂电池SOC估计

被引：52

作者：

田茂飞

安治国

陈星

赵琳

李亚坤

司鑫

机构：

[1] 重庆交通大学机电与车辆工程学院

来源：

储能科学与技术 | 2019年 / 8卷 / 04期

关键词：

SOC估计; 二阶RC模型; 在线参数辨识; 扩展卡尔曼滤波; 自适应扩展卡尔曼滤波;

D O I：

暂无

中图分类号：

TM912 [蓄电池];

学科分类号：

080802 [电力系统及其自动化];

摘要：

SOC的准确估计对提高电池的动态性能和能量利用效率至关重要,估计过程中,模型参数不准确以及系统噪声的不确定性都会对结果产生较大影响。为减小模型参数辨识和系统噪声对SOC估计精度的影响,本文采用二阶RC等效电路模型,结合自适应扩展卡尔曼滤波算法(AEKF)进行锂电池的SOC估计。用带有遗忘因子的最小二乘法对模型参数进行在线辨识,以减小由参数辨识引起的估计误差,AEKF可以对系统和过程噪声进行修正,从而减小噪声对SOC估计的影响。最后分别用EKF和AEKF进行SOC估计并比较其误差,结果表明,AEKF联合最小二乘法参数在线辨识具有更高的精度和更好的适应性。

引用

页码：745 / 750

页数：6

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