基于改进卡尔曼滤波的电池SOC估算

徐颖; 沈英

doi:10.13700/j.bh.1001-5965.2013.0414

基于改进卡尔曼滤波的电池SOC估算

doi: 10.13700/j.bh.1001-5965.2013.0414

徐颖,
沈英

北京航空航天大学机械工程及自动化学院, 北京 100191

详细信息

作者简介:
徐颖（1969- ），女，四川德阳人，副教授，xuying@buaa.edu.cn.

中图分类号: TM912.1
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- 被引次数: 0
出版历程
- 收稿日期: 2013-07-11
- 网络出版日期: 2014-06-20

Improved battery state-of-charge estimation based on Kalman filter

Xu Ying,
Shen Ying

School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 以研究电动汽车动力电池管理系统为背景，以电池荷电状态估算为关键技术，介绍了荷电状态与其主要影响因素的非线性动态关系，建立了二阶RC等效电池模型.在此基础上，考虑了温度对电池内阻的影响，采用卡尔曼滤波算法、改进的安时计量法和开路电压法，结合基于温度的电池模型参数在线辨识，对电池荷电状态进行估算，通过MATLAB仿真，并与基于经验公式的卡尔曼滤波算法进行了对比，平均误差为2.46%，提高了估算精度，验证了算法的可行性和可靠性.
- 荷电状态 /
- 电动汽车 /
- 电池模型 /
- 卡尔曼滤波
Abstract: Set in the research in the battery management system of electric vehicle, the state of charge, as well as the main factors to their nonlinear dynamic relationship, was illustrated and a second-order RC equivalent cell model was established based on the key technology of battery state of charge estimation. After taking the influence of temperature on the battery internal resistance into account, the state of charge of the battery was estimated with Kalman filter algorithm, the improved Ah counting method and the open-circuit voltage method, combined with the online thermal model parameters identification. MATLAB simulation shows that the average error was 2.46% compared with the conventional Kalman filter algorithm, which verifies the feasibility and reliability.
- state-of-charge /
- electrical vehicles /
- battery model /
- Kalman filtering

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参考文献(8)

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