A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFPE |
Texto Completo: | https://repositorio.ufpe.br/handle/123456789/39127 |
Resumo: | In order to decrease the emission of greenhouse gases and propose alternatives to the environmental effect of it, the development and improvement of “green technologies” have received special attention due to their utility to prevent the impacts caused by those gases. Thus, electric vehicles (EVs) were, also, an important advancement in this area. To work, the EVs need a reliable battery source and, for most EVs, a lithium-ion battery is used as a power source. Some advantages of lithium-ion batteries are high specific energy density, high cycle life, and low self-discharge. In the context of Prognostic and Health Management (PHM), estimation of the SOC (State of Charge) – which is the remaining charge within the battery and is defined as the ratio of the residual capacity of the battery to its nominal capacity – based on data-driven methods (e.g. Machine Learning – ML, Deep Neural Networks – DNN) and data storage (e.g. Big Data) has come as a suitable alternative to identify patterns in its degradation over time, also being much less time-consuming than physics of failure (e.g. coulomb counting and open circuit approaches) methods, which needs full discharging to estimate SOC. In this work, a methodology using DNN and Machine Learning (ML) algorithms is proposed to predict battery SOC. At first, the input – current and voltage – and the output – SOC – each given in the form of time series, are replicated using Maximum Entropy Bootstrap (MEB), a sampling technique used with non-stationary time series- this technique is used to further compute confidence interval of the remaining time until the next recharge. Afterward, the input dataset is processed using a windowing model as the pre-processing step; this processed dataset is used to train a DNN model. For purposes of comparison, the data is also fed into an ML model, with each replication training the model. Following the training phase, the predicted SOC, for both the DNN and ML model, is filtered by an Unscented Kalman Filter (UKF), which processes the predicted SOC time series in terms of its mean and covariance. Then, the remaining time until the next recharge is computed and compared with the real discharge time. Finally, the confidence interval of the remaining time until the next discharge is calculated for the DNN and ML models. Analyzing the results, the DNN model, which is performed by the Multi-Layer Perceptron, has better results compared with the other applied methods – Support Vector Machines, Random Forest and XGBoost – with lower root mean squared error results and percentage errors for the remaining time until the next discharge – for both non and postprocessed results. These results are achieved due to the complexity of the DNN model. However, further analysis in terms of the number of layers for the DNN method needs to be operated. For the Random Forest and XGBoost methods, which obtain the worst results, they are, generally applied for classification tasks, explaining the observed results. |
id |
UFPE_725a09153a142776a9a3bd21124bc269 |
---|---|
oai_identifier_str |
oai:repositorio.ufpe.br:123456789/39127 |
network_acronym_str |
UFPE |
network_name_str |
Repositório Institucional da UFPE |
repository_id_str |
2221 |
spelling |
SANTOS, Monalisa Cristina Moura doshttp://lattes.cnpq.br/5432126651780891http://lattes.cnpq.br/5632602851077460LINS, Isis Didier2021-01-26T12:47:02Z2021-01-26T12:47:02Z2020-02-18SANTOS, Monalisa Cristina Moura dos. A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle. 2020. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal de Pernambuco, Recife, 2020.https://repositorio.ufpe.br/handle/123456789/39127In order to decrease the emission of greenhouse gases and propose alternatives to the environmental effect of it, the development and improvement of “green technologies” have received special attention due to their utility to prevent the impacts caused by those gases. Thus, electric vehicles (EVs) were, also, an important advancement in this area. To work, the EVs need a reliable battery source and, for most EVs, a lithium-ion battery is used as a power source. Some advantages of lithium-ion batteries are high specific energy density, high cycle life, and low self-discharge. In the context of Prognostic and Health Management (PHM), estimation of the SOC (State of Charge) – which is the remaining charge within the battery and is defined as the ratio of the residual capacity of the battery to its nominal capacity – based on data-driven methods (e.g. Machine Learning – ML, Deep Neural Networks – DNN) and data storage (e.g. Big Data) has come as a suitable alternative to identify patterns in its degradation over time, also being much less time-consuming than physics of failure (e.g. coulomb counting and open circuit approaches) methods, which needs full discharging to estimate SOC. In this work, a methodology using DNN and Machine Learning (ML) algorithms is proposed to predict battery SOC. At first, the input – current and voltage – and the output – SOC – each given in the form of time series, are replicated using Maximum Entropy Bootstrap (MEB), a sampling technique used with non-stationary time series- this technique is used to further compute confidence interval of the remaining time until the next recharge. Afterward, the input dataset is processed using a windowing model as the pre-processing step; this processed dataset is used to train a DNN model. For purposes of comparison, the data is also fed into an ML model, with each replication training the model. Following the training phase, the predicted SOC, for both the DNN and ML model, is filtered by an Unscented Kalman Filter (UKF), which processes the predicted SOC time series in terms of its mean and covariance. Then, the remaining time until the next recharge is computed and compared with the real discharge time. Finally, the confidence interval of the remaining time until the next discharge is calculated for the DNN and ML models. Analyzing the results, the DNN model, which is performed by the Multi-Layer Perceptron, has better results compared with the other applied methods – Support Vector Machines, Random Forest and XGBoost – with lower root mean squared error results and percentage errors for the remaining time until the next discharge – for both non and postprocessed results. These results are achieved due to the complexity of the DNN model. However, further analysis in terms of the number of layers for the DNN method needs to be operated. For the Random Forest and XGBoost methods, which obtain the worst results, they are, generally applied for classification tasks, explaining the observed results.CNPqA fim de diminuir a emissão de gases de efeito estufa e propor alternativas para os efeitos no meio ambiente causado pelos menos, o desenvolvimento de “tecnologias verdes” tem recebido uma atenção especial devido a sua importância para prevenir e evitar os impactos causados por esses gases. Então, os veículos elétricos (VEs) são um avanço nessa área. Para funcionar, os VEs precisam de uma bateria que seja confiável, sendo as baterias de lítio as mais utilizadas como fonte de energia. Algumas vantagens que as células de lítio possuem são a alta densidade energética específica, um alto ciclo de vida e baixa auto descarga. No contexto de Prognostic and Health Management (PHM), a estimação do estado da carga – que é a carga remanescente da bateria definida pela razão da capacidade residual da bateria e a capacidade nominal – baseada em métodos conduzidos por dados (e.g. Machine Learning – ML, Deep Neural Networks – DNN) e armazenamento de dados (e.g. Big Data) vem como uma alternativa para identificar padrões de degradação através do tempo, sendo um procedimento que usa menos modelos baseados em física de falha (e.g. contagem de coulomb e métodos de circuitos abertos), eles precisam da descarga total para realizar a estimativa do estado da carga. Nesse trabalho, uma metodologia usando DNN e ML foi proposta para fazer a estimativa do estado da carga. Primeiramente, a entrada – corrente e voltagem – e a saída – estado da carga – cada uma dada em formato de série temporal, e, posteriormente, replicada utilizando o Maximum Entropy Bootstrap (MEB), sendo utilizada para realizar a estimativa do intervalo de confiança do tempo até a próxima descarga. Depois, os dados de entrada são processados utilizando um modelo de janelamento como etapa de pré-processamento; estes dados pré-processados são utilizados para treinar o modelo DNN. Depois da previsão do estado da carga, os resultados dos modelos de DNN e ML serão filtrados utilizando o Unscented Kalman Filter (UKF), que processa a série temporal do estado da carga previsto em termos da sua média e covariância, Então, o tempo restante até a próxima descarga é calculado e comparado com o tempo de descarga real. Finalmente, o intervalo de confiança do tempo restante até a próxima descarga é computado para o DNN e o ML. Analisando os resultados e comparando com outros métodos utilizados, o modelo DNN, representado por um Multi-Layer Perceptron, obteve os melhores resultados se comparados com os outros métodos aplicados – Support Vector Machines, Random Forest e XGBoost – com um menor erro médio quadrático e um erro percentual menor para o tempo remanescente até a descarga – para os resultados sem e com pós-processamento. Esses resultados foram alcançados devido a complexidade do modelo DNN. Entretanto, análises posteriores necessitam ser executadas em termos do número de camadas da DNN, entre outros parâmetros. Para o Random Forest e o XGBoost, que obtiveram os piores resultados, eles são, geralmente, aplicados para tarefas de classificação, o que explica, em parte, os resultados obtidos.porUniversidade Federal de PernambucoPrograma de Pos Graduacao em Engenharia de ProducaoUFPEBrasilAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessEngenharia de ProduçãoVeículos elétricosEstado da cargaMachine learningPrognósticoHealth managementA proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesismestradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPECC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufpe.br/bitstream/123456789/39127/2/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82310https://repositorio.ufpe.br/bitstream/123456789/39127/3/license.txtbd573a5ca8288eb7272482765f819534MD53TEXTDISSERTAÇÃO Monalisa Cristina Moura dos Santos.pdf.txtDISSERTAÇÃO Monalisa Cristina Moura dos Santos.pdf.txtExtracted texttext/plain148357https://repositorio.ufpe.br/bitstream/123456789/39127/4/DISSERTA%c3%87%c3%83O%20Monalisa%20Cristina%20Moura%20dos%20Santos.pdf.txtd3a45c3f02a044fbdcc555ff546d6198MD54THUMBNAILDISSERTAÇÃO Monalisa Cristina Moura dos Santos.pdf.jpgDISSERTAÇÃO Monalisa Cristina Moura dos Santos.pdf.jpgGenerated Thumbnailimage/jpeg1198https://repositorio.ufpe.br/bitstream/123456789/39127/5/DISSERTA%c3%87%c3%83O%20Monalisa%20Cristina%20Moura%20dos%20Santos.pdf.jpg6681c4a409466ceb901d3f7c2740d0caMD55ORIGINALDISSERTAÇÃO Monalisa Cristina Moura dos Santos.pdfDISSERTAÇÃO Monalisa Cristina Moura dos Santos.pdfapplication/pdf1697106https://repositorio.ufpe.br/bitstream/123456789/39127/1/DISSERTA%c3%87%c3%83O%20Monalisa%20Cristina%20Moura%20dos%20Santos.pdf174aaf6124c7cb0139096afc4245eea6MD51123456789/391272021-01-27 02:14:29.519oai:repositorio.ufpe.br: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ório InstitucionalPUBhttps://repositorio.ufpe.br/oai/requestattena@ufpe.bropendoar:22212021-01-27T05:14:29Repositório Institucional da UFPE - Universidade Federal de Pernambuco (UFPE)false |
dc.title.pt_BR.fl_str_mv |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle |
title |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle |
spellingShingle |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle SANTOS, Monalisa Cristina Moura dos Engenharia de Produção Veículos elétricos Estado da carga Machine learning Prognóstico Health management |
title_short |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle |
title_full |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle |
title_fullStr |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle |
title_full_unstemmed |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle |
title_sort |
A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle |
author |
SANTOS, Monalisa Cristina Moura dos |
author_facet |
SANTOS, Monalisa Cristina Moura dos |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/5432126651780891 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/5632602851077460 |
dc.contributor.author.fl_str_mv |
SANTOS, Monalisa Cristina Moura dos |
dc.contributor.advisor1.fl_str_mv |
LINS, Isis Didier |
contributor_str_mv |
LINS, Isis Didier |
dc.subject.por.fl_str_mv |
Engenharia de Produção Veículos elétricos Estado da carga Machine learning Prognóstico Health management |
topic |
Engenharia de Produção Veículos elétricos Estado da carga Machine learning Prognóstico Health management |
description |
In order to decrease the emission of greenhouse gases and propose alternatives to the environmental effect of it, the development and improvement of “green technologies” have received special attention due to their utility to prevent the impacts caused by those gases. Thus, electric vehicles (EVs) were, also, an important advancement in this area. To work, the EVs need a reliable battery source and, for most EVs, a lithium-ion battery is used as a power source. Some advantages of lithium-ion batteries are high specific energy density, high cycle life, and low self-discharge. In the context of Prognostic and Health Management (PHM), estimation of the SOC (State of Charge) – which is the remaining charge within the battery and is defined as the ratio of the residual capacity of the battery to its nominal capacity – based on data-driven methods (e.g. Machine Learning – ML, Deep Neural Networks – DNN) and data storage (e.g. Big Data) has come as a suitable alternative to identify patterns in its degradation over time, also being much less time-consuming than physics of failure (e.g. coulomb counting and open circuit approaches) methods, which needs full discharging to estimate SOC. In this work, a methodology using DNN and Machine Learning (ML) algorithms is proposed to predict battery SOC. At first, the input – current and voltage – and the output – SOC – each given in the form of time series, are replicated using Maximum Entropy Bootstrap (MEB), a sampling technique used with non-stationary time series- this technique is used to further compute confidence interval of the remaining time until the next recharge. Afterward, the input dataset is processed using a windowing model as the pre-processing step; this processed dataset is used to train a DNN model. For purposes of comparison, the data is also fed into an ML model, with each replication training the model. Following the training phase, the predicted SOC, for both the DNN and ML model, is filtered by an Unscented Kalman Filter (UKF), which processes the predicted SOC time series in terms of its mean and covariance. Then, the remaining time until the next recharge is computed and compared with the real discharge time. Finally, the confidence interval of the remaining time until the next discharge is calculated for the DNN and ML models. Analyzing the results, the DNN model, which is performed by the Multi-Layer Perceptron, has better results compared with the other applied methods – Support Vector Machines, Random Forest and XGBoost – with lower root mean squared error results and percentage errors for the remaining time until the next discharge – for both non and postprocessed results. These results are achieved due to the complexity of the DNN model. However, further analysis in terms of the number of layers for the DNN method needs to be operated. For the Random Forest and XGBoost methods, which obtain the worst results, they are, generally applied for classification tasks, explaining the observed results. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-02-18 |
dc.date.accessioned.fl_str_mv |
2021-01-26T12:47:02Z |
dc.date.available.fl_str_mv |
2021-01-26T12:47:02Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
SANTOS, Monalisa Cristina Moura dos. A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle. 2020. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal de Pernambuco, Recife, 2020. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufpe.br/handle/123456789/39127 |
identifier_str_mv |
SANTOS, Monalisa Cristina Moura dos. A proposed methodology using artificial intelligence to estimate the state of charge for batteries of electric vehicle. 2020. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal de Pernambuco, Recife, 2020. |
url |
https://repositorio.ufpe.br/handle/123456789/39127 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pos Graduacao em Engenharia de Producao |
dc.publisher.initials.fl_str_mv |
UFPE |
dc.publisher.country.fl_str_mv |
Brasil |
publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFPE instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
instname_str |
Universidade Federal de Pernambuco (UFPE) |
instacron_str |
UFPE |
institution |
UFPE |
reponame_str |
Repositório Institucional da UFPE |
collection |
Repositório Institucional da UFPE |
bitstream.url.fl_str_mv |
https://repositorio.ufpe.br/bitstream/123456789/39127/2/license_rdf https://repositorio.ufpe.br/bitstream/123456789/39127/3/license.txt https://repositorio.ufpe.br/bitstream/123456789/39127/4/DISSERTA%c3%87%c3%83O%20Monalisa%20Cristina%20Moura%20dos%20Santos.pdf.txt https://repositorio.ufpe.br/bitstream/123456789/39127/5/DISSERTA%c3%87%c3%83O%20Monalisa%20Cristina%20Moura%20dos%20Santos.pdf.jpg https://repositorio.ufpe.br/bitstream/123456789/39127/1/DISSERTA%c3%87%c3%83O%20Monalisa%20Cristina%20Moura%20dos%20Santos.pdf |
bitstream.checksum.fl_str_mv |
e39d27027a6cc9cb039ad269a5db8e34 bd573a5ca8288eb7272482765f819534 d3a45c3f02a044fbdcc555ff546d6198 6681c4a409466ceb901d3f7c2740d0ca 174aaf6124c7cb0139096afc4245eea6 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFPE - Universidade Federal de Pernambuco (UFPE) |
repository.mail.fl_str_mv |
attena@ufpe.br |
_version_ |
1802310827093524480 |