A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/100804 https://doi.org/10.3390/en14123373 |
Resumo: | Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle. |
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A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric VehiclesBatteryElectric vehicleEnergy managementHybrid energy storage systemSupercapacitorHybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle.2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100804http://hdl.handle.net/10316/100804https://doi.org/10.3390/en14123373eng1996-1073Nguyen, Hoai-Linh T.Nguyễn, Bảo-HuyVo-Duy, ThanhTrovão, João Pedro F.info: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:RCAAP2022-07-12T20:31:46Zoai:estudogeral.uc.pt:10316/100804Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:06.918360Repositório 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 |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles |
title |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles |
spellingShingle |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles Nguyen, Hoai-Linh T. Battery Electric vehicle Energy management Hybrid energy storage system Supercapacitor |
title_short |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles |
title_full |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles |
title_fullStr |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles |
title_full_unstemmed |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles |
title_sort |
A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles |
author |
Nguyen, Hoai-Linh T. |
author_facet |
Nguyen, Hoai-Linh T. Nguyễn, Bảo-Huy Vo-Duy, Thanh Trovão, João Pedro F. |
author_role |
author |
author2 |
Nguyễn, Bảo-Huy Vo-Duy, Thanh Trovão, João Pedro F. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Nguyen, Hoai-Linh T. Nguyễn, Bảo-Huy Vo-Duy, Thanh Trovão, João Pedro F. |
dc.subject.por.fl_str_mv |
Battery Electric vehicle Energy management Hybrid energy storage system Supercapacitor |
topic |
Battery Electric vehicle Energy management Hybrid energy storage system Supercapacitor |
description |
Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/100804 http://hdl.handle.net/10316/100804 https://doi.org/10.3390/en14123373 |
url |
http://hdl.handle.net/10316/100804 https://doi.org/10.3390/en14123373 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1996-1073 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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 |
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1799134076288892928 |