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The Scientific World Journal
Volume 2014 (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.

Abstract

In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of and values (KF’s parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune and parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.