Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System
Joint Authors
Leach, Mark
Man, Ka Lok
Ting, T. O.
Lim, Eng Gee
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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 Q and R 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 Q and R 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.
American Psychological Association (APA)
Ting, T. O.& Man, Ka Lok& Lim, Eng Gee& Leach, Mark. 2014. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1048600
Modern Language Association (MLA)
Ting, T. O.…[et al.]. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1048600
American Medical Association (AMA)
Ting, T. O.& Man, Ka Lok& Lim, Eng Gee& Leach, Mark. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1048600
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048600