Battery State-of-charge Estimation based on H∞ Filter for Hybrid Electric Vehicle

被引:0
作者
Yan, Jingyu [1 ]
Xu, Guoqing [1 ]
Xu, Yangsheng [1 ]
Xie, Benliang [2 ]
机构
[1] Chinese Univ Hong Kong, Inst Adv Integrat Technol, Chinese Acad Sci, Shenzhen, Peoples R China
[2] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
来源
2008 10TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION: ICARV 2008, VOLS 1-4 | 2008年
关键词
State of charge; H-infinity filter; Battery management system;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
State-of-charge (SOC) estimation is the most difficult problem in battery management system, which is one of the key component of electric vehicle and hybrid electric vehicle. Suffered from the non-zero mean noise and uncertain model parameters in practice, the conventional current integral and Kalman filter estimation methods can not achieve the required accuracy, even causing nonconvergent results. The essential difficulties to apply current integral and Kalman filter to solve SOC estimation problem in colored noise and time-variant battery system are analyzed. H-infinity filter, an estimator designed to handle the estimation problem in noised and uncertain situation, is then applied to calculate SOC online. The simulation experiment based on a typical battery model verifies the availability and efficiency of the proposed method.
引用
收藏
页码:464 / +
页数:2
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