State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Jinhao Meng, Guangzhao Luo, Fei Gao

科研成果: 书/报告/会议事项章节会议稿件同行评审

14 引用 (Scopus)

摘要

A new method 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.

源语言英语
主期刊名IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781479942398
DOI
出版状态已出版 - 30 10月 2014
活动2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Beijing, 中国
期限: 31 8月 20143 9月 2014

出版系列

姓名IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

会议

会议2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014
国家/地区中国
Beijing
时期31/08/143/09/14

指纹

探究 'State-of-charge estimation for lithium-ion battery using AUKF and LSSVM' 的科研主题。它们共同构成独一无二的指纹。

引用此