Data mining approach for range prediction of electric vehicle

Detalhes bibliográficos
Autor(a) principal: Ferreira, João C.
Data de Publicação: 2012
Outros Autores: Monteiro, Vítor Duarte Fernandes, Afonso, João L.
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/19439
Resumo: Our work proposal is based on the past driving data that are stored in a driver profile, and using real time information about the Electric Vehicle parameters (e.g. speed and energy stored in the batteries), combined with external parameters (e.g. condi-tions of roads, traffic, and weather), determine the range autonomy accurately, taking into account the historical driver behavior. The driver profile is based on the stored data, which acts as training set for a Data Mining approach, in order to estimate the Electric Vehicle range. The Data Mining approach uses a regression model aiming to find the better range autonomy, which is used to represent the current Electric Vehicle range autonomy on a map.
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spelling Data mining approach for range prediction of electric vehicleRange predictionData miningDriver profileRange anxiety problemElectric vehicleOur work proposal is based on the past driving data that are stored in a driver profile, and using real time information about the Electric Vehicle parameters (e.g. speed and energy stored in the batteries), combined with external parameters (e.g. condi-tions of roads, traffic, and weather), determine the range autonomy accurately, taking into account the historical driver behavior. The driver profile is based on the stored data, which acts as training set for a Data Mining approach, in order to estimate the Electric Vehicle range. The Data Mining approach uses a regression model aiming to find the better range autonomy, which is used to represent the current Electric Vehicle range autonomy on a map.Fundação para a Ciência e a Tecnologia (FCT)Universidade do MinhoFerreira, João C.Monteiro, Vítor Duarte FernandesAfonso, João L.2012-032012-03-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/19439enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-11T04:49:16Zoai:repositorium.sdum.uminho.pt:1822/19439Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T04:49:16Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Data mining approach for range prediction of electric vehicle
title Data mining approach for range prediction of electric vehicle
spellingShingle Data mining approach for range prediction of electric vehicle
Ferreira, João C.
Range prediction
Data mining
Driver profile
Range anxiety problem
Electric vehicle
title_short Data mining approach for range prediction of electric vehicle
title_full Data mining approach for range prediction of electric vehicle
title_fullStr Data mining approach for range prediction of electric vehicle
title_full_unstemmed Data mining approach for range prediction of electric vehicle
title_sort Data mining approach for range prediction of electric vehicle
author Ferreira, João C.
author_facet Ferreira, João C.
Monteiro, Vítor Duarte Fernandes
Afonso, João L.
author_role author
author2 Monteiro, Vítor Duarte Fernandes
Afonso, João L.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Ferreira, João C.
Monteiro, Vítor Duarte Fernandes
Afonso, João L.
dc.subject.por.fl_str_mv Range prediction
Data mining
Driver profile
Range anxiety problem
Electric vehicle
topic Range prediction
Data mining
Driver profile
Range anxiety problem
Electric vehicle
description Our work proposal is based on the past driving data that are stored in a driver profile, and using real time information about the Electric Vehicle parameters (e.g. speed and energy stored in the batteries), combined with external parameters (e.g. condi-tions of roads, traffic, and weather), determine the range autonomy accurately, taking into account the historical driver behavior. The driver profile is based on the stored data, which acts as training set for a Data Mining approach, in order to estimate the Electric Vehicle range. The Data Mining approach uses a regression model aiming to find the better range autonomy, which is used to represent the current Electric Vehicle range autonomy on a map.
publishDate 2012
dc.date.none.fl_str_mv 2012-03
2012-03-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/19439
url http://hdl.handle.net/1822/19439
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.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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