State-of-Charge Estimation for Lithium-ion Battery using Busse's Adaptive Unscented Kalman Filter

被引：0
|
作者：
Yao, Low Wen [1 ]
Aziz, J. A. [1 ]
Idris, N. R. N. [1 ]
机构：
[1] Univ Teknol Malaysia, Fac Elect Engn, Dept Elect Power Engn, Power Elect Drive Res Grp, Skudai 81310, Johor, Malaysia
来源：
2015 IEEE CONFERENCE ON ENERGY CONVERSION (CENCON) | 2015年
关键词：
state-of-charge; adaptive unscented Kalman filter; lithium-ion battery; LEAD-ACID-BATTERIES; MANAGEMENT-SYSTEMS; ELECTRIC VEHICLES; PARAMETER-ESTIMATION; PART; MODEL; PACKS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
State-of-charge estimation of rechargeable battery is vital to maximize the battery performance and ensure the safe operating condition. This paper presents state-of-charge estimation method for lithium-ion battery using adaptive unscented Kalman Filter. In this aspect, Busse's adaptive rule is implemented to update the process noise covariance of the Kalman filter. Compared with the existing adaptive rules, Busse's rule is relatively simpler and it doesn't require huge memory capacity for storing the voltage residual. The accuracy of the proposed method is verified through experimental studies. A comparison with the unscented Kalman filter algorithms is made to compare the accuracy of each algorithm.
引用
收藏
页码：227 / 232
页数：6
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