Cargando…
Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138764/ https://www.ncbi.nlm.nih.gov/pubmed/25162041 http://dx.doi.org/10.1155/2014/176052 |
_version_ | 1782331277267959808 |
---|---|
author | Ting, T. O. Man, Ka Lok Lim, Eng Gee Leach, Mark |
author_facet | Ting, T. O. Man, Ka Lok Lim, Eng Gee Leach, Mark |
author_sort | Ting, T. O. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4138764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-41387642014-08-26 Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System Ting, T. O. Man, Ka Lok Lim, Eng Gee Leach, Mark ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014 2014-08-05 /pmc/articles/PMC4138764/ /pubmed/25162041 http://dx.doi.org/10.1155/2014/176052 Text en Copyright © 2014 T. O. Ting et al. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Research Article 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 |
title | Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System |
title_full | Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System |
title_fullStr | Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System |
title_full_unstemmed | Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System |
title_short | Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System |
title_sort | tuning of kalman filter parameters via genetic algorithm for state-of-charge estimation in battery management system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138764/ https://www.ncbi.nlm.nih.gov/pubmed/25162041 http://dx.doi.org/10.1155/2014/176052 |
work_keys_str_mv | AT tingto tuningofkalmanfilterparametersviageneticalgorithmforstateofchargeestimationinbatterymanagementsystem AT mankalok tuningofkalmanfilterparametersviageneticalgorithmforstateofchargeestimationinbatterymanagementsystem AT limenggee tuningofkalmanfilterparametersviageneticalgorithmforstateofchargeestimationinbatterymanagementsystem AT leachmark tuningofkalmanfilterparametersviageneticalgorithmforstateofchargeestimationinbatterymanagementsystem |