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The Scientific World Journal
Volume 2014, Article ID 176052, 11 pages
http://dx.doi.org/10.1155/2014/176052
Research Article

Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System

1Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, No. 111, Ren’ai Road, HET, SIP, Suzhou, Jiangsu 215123, China
2Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, No. 111, Ren’ai Road, HET, SIP, Suzhou, Jiangsu 215123, China
3Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea

Received 22 April 2014; Revised 3 June 2014; Accepted 4 June 2014; Published 5 August 2014

Academic Editor: Su Fong Chien

Copyright © 2014 T. O. Ting et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

T. O. Ting, Ka Lok Man, Eng Gee Lim, and Mark Leach, “Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System,” The Scientific World Journal, vol. 2014, Article ID 176052, 11 pages, 2014. https://doi.org/10.1155/2014/176052.