Machine learning systems applied in satellite lithium-ion battery set impedance estimation
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do INPE |
Texto Completo: | http://urlib.net/sid.inpe.br/mtc-m21c/2018/04.27.23.39 |
Resumo: | In this work, the internal impedance of the lithium-ion battery pack, an essential measure of the degradation level of the batteries, is estimated employing ensembles of machine learning models. In this study, we take the supervised learning techniques Multi-Layer Perceptron bagging neural network and gradient tree boosting into account. Characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the modeling and training phases. During this process, the architecture of the ensembles and the configuration of their base learners are tuned through validation iterations. Finally, with the application of statistical testing and similarity analysis techniques, the best ensembles of models are examined and compared to other methods found in the literature. Results indicate that our approach is a suitable manner to estimate the internal impedance of batteries. |
id |
INPE_641adbe157250f0bbeabc0726992a527 |
---|---|
oai_identifier_str |
oai:urlib.net:sid.inpe.br/mtc-m21c/2018/04.27.23.39.17-0 |
network_acronym_str |
INPE |
network_name_str |
Biblioteca Digital de Teses e Dissertações do INPE |
spelling |
info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisMachine learning systems applied in satellite lithium-ion battery set impedance estimationEstimativa da impedância de conjuntos de baterias de lítio-íon por meio de aprendizado de máquina2018-04-02Marcos Gonçalves QuilesReinaldo Roberto RosaElcio Hideiti ShiguemoriWlamir Olivares Loesch ViannaMarcio Porto BasgaluppThiago Henrique Rizzi DonatoInstituto Nacional de Pesquisas Espaciais (INPE)Programa de Pós-Graduação do INPE em Computação AplicadaINPEBRlithion-ion batterystate of chargegradient tree boostingmulti layer perceptronbateria lítio-íonestado de cargaIn this work, the internal impedance of the lithium-ion battery pack, an essential measure of the degradation level of the batteries, is estimated employing ensembles of machine learning models. In this study, we take the supervised learning techniques Multi-Layer Perceptron bagging neural network and gradient tree boosting into account. Characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the modeling and training phases. During this process, the architecture of the ensembles and the configuration of their base learners are tuned through validation iterations. Finally, with the application of statistical testing and similarity analysis techniques, the best ensembles of models are examined and compared to other methods found in the literature. Results indicate that our approach is a suitable manner to estimate the internal impedance of batteries.Neste trabalho, a impedância interna de um conjunto de baterias lítio-íon (uma importante medida do nível de degradação) é estimada por meio de conjuntos de modelos de aprendizado supervisionado tais como: rede neural tipo MLP (Multi- Layer Perceptron) e Gradient Tree Boosting. Para isto, características do sistema de alimentação elétrica, em que o conjunto de baterias está inserido, são extraídas e utilizadas na construção de conjuntos de modelos supervisionados (MLP e xgBoost). Ao longo deste processo, a arquitetura de tais conjuntos de modelos e suas respectivas configurações são ajustados por meio de validações. Finalmente, com a aplicação de técnicas de teste e verificação estatística, as acurácias dos modelos são calculadas e testes comparativos são conduzidos. Os resultados obtidos mostram que a abordagem proposta é adequada para o problema de estimativa da impendância de baterias.http://urlib.net/sid.inpe.br/mtc-m21c/2018/04.27.23.39info:eu-repo/semantics/openAccessengreponame:Biblioteca Digital de Teses e Dissertações do INPEinstname:Instituto Nacional de Pesquisas Espaciais (INPE)instacron:INPE2021-07-31T06:55:45Zoai:urlib.net:sid.inpe.br/mtc-m21c/2018/04.27.23.39.17-0Biblioteca Digital de Teses e Dissertaçõeshttp://bibdigital.sid.inpe.br/PUBhttp://bibdigital.sid.inpe.br/col/iconet.com.br/banon/2003/11.21.21.08/doc/oai.cgiopendoar:32772021-07-31 06:55:45.531Biblioteca Digital de Teses e Dissertações do INPE - Instituto Nacional de Pesquisas Espaciais (INPE)false |
dc.title.en.fl_str_mv |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation |
dc.title.alternative.pt.fl_str_mv |
Estimativa da impedância de conjuntos de baterias de lítio-íon por meio de aprendizado de máquina |
title |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation |
spellingShingle |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation Thiago Henrique Rizzi Donato |
title_short |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation |
title_full |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation |
title_fullStr |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation |
title_full_unstemmed |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation |
title_sort |
Machine learning systems applied in satellite lithium-ion battery set impedance estimation |
author |
Thiago Henrique Rizzi Donato |
author_facet |
Thiago Henrique Rizzi Donato |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Marcos Gonçalves Quiles |
dc.contributor.referee1.fl_str_mv |
Reinaldo Roberto Rosa |
dc.contributor.referee2.fl_str_mv |
Elcio Hideiti Shiguemori |
dc.contributor.referee3.fl_str_mv |
Wlamir Olivares Loesch Vianna |
dc.contributor.referee4.fl_str_mv |
Marcio Porto Basgalupp |
dc.contributor.author.fl_str_mv |
Thiago Henrique Rizzi Donato |
contributor_str_mv |
Marcos Gonçalves Quiles Reinaldo Roberto Rosa Elcio Hideiti Shiguemori Wlamir Olivares Loesch Vianna Marcio Porto Basgalupp |
dc.description.abstract.por.fl_txt_mv |
In this work, the internal impedance of the lithium-ion battery pack, an essential measure of the degradation level of the batteries, is estimated employing ensembles of machine learning models. In this study, we take the supervised learning techniques Multi-Layer Perceptron bagging neural network and gradient tree boosting into account. Characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the modeling and training phases. During this process, the architecture of the ensembles and the configuration of their base learners are tuned through validation iterations. Finally, with the application of statistical testing and similarity analysis techniques, the best ensembles of models are examined and compared to other methods found in the literature. Results indicate that our approach is a suitable manner to estimate the internal impedance of batteries. Neste trabalho, a impedância interna de um conjunto de baterias lítio-íon (uma importante medida do nível de degradação) é estimada por meio de conjuntos de modelos de aprendizado supervisionado tais como: rede neural tipo MLP (Multi- Layer Perceptron) e Gradient Tree Boosting. Para isto, características do sistema de alimentação elétrica, em que o conjunto de baterias está inserido, são extraídas e utilizadas na construção de conjuntos de modelos supervisionados (MLP e xgBoost). Ao longo deste processo, a arquitetura de tais conjuntos de modelos e suas respectivas configurações são ajustados por meio de validações. Finalmente, com a aplicação de técnicas de teste e verificação estatística, as acurácias dos modelos são calculadas e testes comparativos são conduzidos. Os resultados obtidos mostram que a abordagem proposta é adequada para o problema de estimativa da impendância de baterias. |
description |
In this work, the internal impedance of the lithium-ion battery pack, an essential measure of the degradation level of the batteries, is estimated employing ensembles of machine learning models. In this study, we take the supervised learning techniques Multi-Layer Perceptron bagging neural network and gradient tree boosting into account. Characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the modeling and training phases. During this process, the architecture of the ensembles and the configuration of their base learners are tuned through validation iterations. Finally, with the application of statistical testing and similarity analysis techniques, the best ensembles of models are examined and compared to other methods found in the literature. Results indicate that our approach is a suitable manner to estimate the internal impedance of batteries. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-04-02 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
status_str |
publishedVersion |
format |
masterThesis |
dc.identifier.uri.fl_str_mv |
http://urlib.net/sid.inpe.br/mtc-m21c/2018/04.27.23.39 |
url |
http://urlib.net/sid.inpe.br/mtc-m21c/2018/04.27.23.39 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Instituto Nacional de Pesquisas Espaciais (INPE) |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação do INPE em Computação Aplicada |
dc.publisher.initials.fl_str_mv |
INPE |
dc.publisher.country.fl_str_mv |
BR |
publisher.none.fl_str_mv |
Instituto Nacional de Pesquisas Espaciais (INPE) |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do INPE instname:Instituto Nacional de Pesquisas Espaciais (INPE) instacron:INPE |
reponame_str |
Biblioteca Digital de Teses e Dissertações do INPE |
collection |
Biblioteca Digital de Teses e Dissertações do INPE |
instname_str |
Instituto Nacional de Pesquisas Espaciais (INPE) |
instacron_str |
INPE |
institution |
INPE |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do INPE - Instituto Nacional de Pesquisas Espaciais (INPE) |
repository.mail.fl_str_mv |
|
publisher_program_txtF_mv |
Programa de Pós-Graduação do INPE em Computação Aplicada |
contributor_advisor1_txtF_mv |
Marcos Gonçalves Quiles |
_version_ |
1706809361157849088 |