Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm
Main Author: | |
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Publication Date: | 2019 |
Other Authors: | , , |
Format: | Article |
Language: | eng |
Source: | TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512019000100149 |
Summary: | ABSTRACT In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is utilized for the mathematical modeling of the Lithium-ion Polymer batteries lifetime used on mobile devices. The model is also parameterized using the conventional procedure, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The simulations results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and it is also a more agile process than the conventional one, been less subject to subjective aspects. |
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Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithmparameter estimationgenetic algorithm meta-heuristicmathematical modelingABSTRACT In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is utilized for the mathematical modeling of the Lithium-ion Polymer batteries lifetime used on mobile devices. The model is also parameterized using the conventional procedure, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The simulations results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and it is also a more agile process than the conventional one, been less subject to subjective aspects.Sociedade Brasileira de Matemática Aplicada e Computacional2019-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512019000100149TEMA (São Carlos) v.20 n.1 2019reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)instname:Sociedade Brasileira de Matemática Aplicada e Computacionalinstacron:SBMAC10.5540/tema.2019.020.01.0149info:eu-repo/semantics/openAccessBINELO,M. F. B.SAUSEN,A. T. Z. R.SAUSEN,P. S.BINELO,M. O.eng2019-06-07T00:00:00Zoai:scielo:S2179-84512019000100149Revistahttp://www.scielo.br/temaPUBhttps://old.scielo.br/oai/scielo-oai.phpcastelo@icmc.usp.br2179-84511677-1966opendoar:2019-06-07T00:00TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacionalfalse |
dc.title.none.fl_str_mv |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm |
title |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm |
spellingShingle |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm BINELO,M. F. B. parameter estimation genetic algorithm meta-heuristic mathematical modeling |
title_short |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm |
title_full |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm |
title_fullStr |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm |
title_full_unstemmed |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm |
title_sort |
Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm |
author |
BINELO,M. F. B. |
author_facet |
BINELO,M. F. B. SAUSEN,A. T. Z. R. SAUSEN,P. S. BINELO,M. O. |
author_role |
author |
author2 |
SAUSEN,A. T. Z. R. SAUSEN,P. S. BINELO,M. O. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
BINELO,M. F. B. SAUSEN,A. T. Z. R. SAUSEN,P. S. BINELO,M. O. |
dc.subject.por.fl_str_mv |
parameter estimation genetic algorithm meta-heuristic mathematical modeling |
topic |
parameter estimation genetic algorithm meta-heuristic mathematical modeling |
description |
ABSTRACT In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is utilized for the mathematical modeling of the Lithium-ion Polymer batteries lifetime used on mobile devices. The model is also parameterized using the conventional procedure, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The simulations results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and it is also a more agile process than the conventional one, been less subject to subjective aspects. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512019000100149 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512019000100149 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5540/tema.2019.020.01.0149 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
dc.source.none.fl_str_mv |
TEMA (São Carlos) v.20 n.1 2019 reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) instname:Sociedade Brasileira de Matemática Aplicada e Computacional instacron:SBMAC |
instname_str |
Sociedade Brasileira de Matemática Aplicada e Computacional |
instacron_str |
SBMAC |
institution |
SBMAC |
reponame_str |
TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) |
collection |
TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) |
repository.name.fl_str_mv |
TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacional |
repository.mail.fl_str_mv |
castelo@icmc.usp.br |
_version_ |
1752122220615827456 |