State-of-Charge Estimation for Lithium-ion Battery Using AUKF and LSSVM

被引：0
|
作者：
Meng, Jinhao [1 ]
Luo, Guangzhao [1 ]
Gao, Fei
机构：
[1] Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China
来源：
2014 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO (ITEC) ASIA-PACIFIC 2014 | 2014年
关键词：
Battery; adaptive unscented Kalman filter (AUKF); least squares support vector machine (LSSVM); state of charge (SOC);
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：
A new mothed based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.
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页数：6
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