Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction

[EN] Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range,...

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Authors: Pla Moreno, Benjamín|||0000-0001-9238-2939, Bares-Moreno, Pau|||0000-0001-9672-0819, Nakaema-Aronis, Andre|||0000-0001-9599-7736, Anuratha, Sanjith
Format: article
Publication Date:2024
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/220393
Online Access:https://riunet.upv.es/handle/10251/220393
Access Level:Embargoed access
Keyword:Battery electric vehicles
Smart home energy management system
Bidirectional charging
Reinforcement learning
Smart grid
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spelling Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward inductionPla Moreno, Benjamín|||0000-0001-9238-2939Bares-Moreno, Pau|||0000-0001-9672-0819Nakaema-Aronis, Andre|||0000-0001-9599-7736Anuratha, SanjithBattery electric vehiclesSmart home energy management systemBidirectional chargingReinforcement learningSmart grid[EN] Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range, two of the major concerns of BEV costumers. While the energy storing capabilities of BEVs usually exceed commuting requirements, batteries can be also utilized for home energy management system using bi-directional charging technology. This paper introduces an efficient energy management system for a smart home with BEVs and a bidirectional charger by addressing the corresponding optimal control problem of deciding the battery charging and discharging strategy to minimize energy expenditure and cost. To this aim, the electricity price and expenditure of upcoming weeks is forecasted using data from the present week, and a model of the complete system is used to find the optimal solutions by means of backward induction in a receding horizon approach. The proposed strategy relies on currently available information about the home and vehicle energy expenditure and energy prices in the recent past. The results of the study show a reduction of the electricity cost above 20% in the considered use-case.The authors acknowledge the Agencia Valenciana de la Innovacion, through project INNEST/2022/368, for supporting the research and development of intelligent recharging technologies for sustainable mobility.ElsevierInstituto Universitario de Investigación CMT - Clean Mobility & ThermofluidsDepartamento de Máquinas y Motores TérmicosEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialAgència Valenciana de la InnovacióRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-10-1520252025-04-0820262026-07-31journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/220393reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAGENCIA VALENCIANA DE LA INNOVACION AGENCIA VALENCIANA DE LA INNOVACION INNEST%2F2022%2F368 INVESTIGACIÓN Y DESARROLLO DE TECNOLOGÍAS INTELIGENTES PARA RECARGA EN MOVILIDAD SOSTENIBLE (CLEANERGY)embargoed accesshttp://purl.org/coar/access_right/c_f1cfReconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccessoai:riunet.upv.es:10251/2203932026-06-13T07:49:27Z
dc.title.none.fl_str_mv Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
title Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
spellingShingle Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
Pla Moreno, Benjamín|||0000-0001-9238-2939
Battery electric vehicles
Smart home energy management system
Bidirectional charging
Reinforcement learning
Smart grid
title_short Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
title_full Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
title_fullStr Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
title_full_unstemmed Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
title_sort Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction
dc.creator.none.fl_str_mv Pla Moreno, Benjamín|||0000-0001-9238-2939
Bares-Moreno, Pau|||0000-0001-9672-0819
Nakaema-Aronis, Andre|||0000-0001-9599-7736
Anuratha, Sanjith
author Pla Moreno, Benjamín|||0000-0001-9238-2939
author_facet Pla Moreno, Benjamín|||0000-0001-9238-2939
Bares-Moreno, Pau|||0000-0001-9672-0819
Nakaema-Aronis, Andre|||0000-0001-9599-7736
Anuratha, Sanjith
author_role author
author2 Bares-Moreno, Pau|||0000-0001-9672-0819
Nakaema-Aronis, Andre|||0000-0001-9599-7736
Anuratha, Sanjith
author2_role author
author
author
dc.contributor.none.fl_str_mv Instituto Universitario de Investigación CMT - Clean Mobility & Thermofluids
Departamento de Máquinas y Motores Térmicos
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Agència Valenciana de la Innovació
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Battery electric vehicles
Smart home energy management system
Bidirectional charging
Reinforcement learning
Smart grid
topic Battery electric vehicles
Smart home energy management system
Bidirectional charging
Reinforcement learning
Smart grid
description [EN] Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range, two of the major concerns of BEV costumers. While the energy storing capabilities of BEVs usually exceed commuting requirements, batteries can be also utilized for home energy management system using bi-directional charging technology. This paper introduces an efficient energy management system for a smart home with BEVs and a bidirectional charger by addressing the corresponding optimal control problem of deciding the battery charging and discharging strategy to minimize energy expenditure and cost. To this aim, the electricity price and expenditure of upcoming weeks is forecasted using data from the present week, and a model of the complete system is used to find the optimal solutions by means of backward induction in a receding horizon approach. The proposed strategy relies on currently available information about the home and vehicle energy expenditure and energy prices in the recent past. The results of the study show a reduction of the electricity cost above 20% in the considered use-case.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-10-15
2025
2025-04-08
2026
2026-07-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/220393
url https://riunet.upv.es/handle/10251/220393
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv AGENCIA VALENCIANA DE LA INNOVACION AGENCIA VALENCIANA DE LA INNOVACION INNEST%2F2022%2F368 INVESTIGACIÓN Y DESARROLLO DE TECNOLOGÍAS INTELIGENTES PARA RECARGA EN MOVILIDAD SOSTENIBLE (CLEANERGY)
dc.rights.none.fl_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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