Adaptive electric vehicle battery charging with multi-objective reinforcement learning

Autores
Trimboli, Maximiliano; Avila, Luis
Año de publicación
2025
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Fast and safe battery charging remains a critical barrier to large-scale EV adoption, as high charging rates accelerate battery degradation and increase thermal risk. Traditional control strategies often rely on static heuristics or single-objective optimization, limiting their ability to manage trade-offs between speed, safety, and battery longevity. This work proposes a deep multi-objective reinforcement learning (MORL) framework for optimal EV battery charging. The proposed agent learns policies that dynamically balance competing objectives—such as minimizing charge time and thermal stress—based on user-defined preferences. Unlike scalar reward methods, MORL captures trade-offs explicitly and adapts charging behavior to context. Experimental results show the policy’s adaptability: faster charging is achieved when temperature constraints are relaxed, while more conservative profiles emerge when battery longevity is prioritized. This highlights the potential of MORL to enhance both the safety and efficiency of EV charging.
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Multi-objective
DQN
Scalarization
Lithium-ion battery
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/191534
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/191534
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Adaptive electric vehicle battery charging with multi-objective reinforcement learningTrimboli, MaximilianoAvila, LuisCiencias InformáticasMulti-objectiveDQNScalarizationLithium-ion batteryFast and safe battery charging remains a critical barrier to large-scale EV adoption, as high charging rates accelerate battery degradation and increase thermal risk. Traditional control strategies often rely on static heuristics or single-objective optimization, limiting their ability to manage trade-offs between speed, safety, and battery longevity. This work proposes a deep multi-objective reinforcement learning (MORL) framework for optimal EV battery charging. The proposed agent learns policies that dynamically balance competing objectives—such as minimizing charge time and thermal stress—based on user-defined preferences. Unlike scalar reward methods, MORL captures trade-offs explicitly and adapts charging behavior to context. Experimental results show the policy’s adaptability: faster charging is achieved when temperature constraints are relaxed, while more conservative profiles emerge when battery longevity is prioritized. This highlights the potential of MORL to enhance both the safety and efficiency of EV charging.Red de Universidades con Carreras en Informática2025-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf34-43http://sedici.unlp.edu.ar/handle/10915/191534enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-8258-99-7info:eu-repo/semantics/reference/hdl/10915/189846info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-03-26T09:21:32Zoai:sedici.unlp.edu.ar:10915/191534Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-03-26 09:21:32.375SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Adaptive electric vehicle battery charging with multi-objective reinforcement learning
title Adaptive electric vehicle battery charging with multi-objective reinforcement learning
spellingShingle Adaptive electric vehicle battery charging with multi-objective reinforcement learning
Trimboli, Maximiliano
Ciencias Informáticas
Multi-objective
DQN
Scalarization
Lithium-ion battery
title_short Adaptive electric vehicle battery charging with multi-objective reinforcement learning
title_full Adaptive electric vehicle battery charging with multi-objective reinforcement learning
title_fullStr Adaptive electric vehicle battery charging with multi-objective reinforcement learning
title_full_unstemmed Adaptive electric vehicle battery charging with multi-objective reinforcement learning
title_sort Adaptive electric vehicle battery charging with multi-objective reinforcement learning
dc.creator.none.fl_str_mv Trimboli, Maximiliano
Avila, Luis
author Trimboli, Maximiliano
author_facet Trimboli, Maximiliano
Avila, Luis
author_role author
author2 Avila, Luis
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Multi-objective
DQN
Scalarization
Lithium-ion battery
topic Ciencias Informáticas
Multi-objective
DQN
Scalarization
Lithium-ion battery
dc.description.none.fl_txt_mv Fast and safe battery charging remains a critical barrier to large-scale EV adoption, as high charging rates accelerate battery degradation and increase thermal risk. Traditional control strategies often rely on static heuristics or single-objective optimization, limiting their ability to manage trade-offs between speed, safety, and battery longevity. This work proposes a deep multi-objective reinforcement learning (MORL) framework for optimal EV battery charging. The proposed agent learns policies that dynamically balance competing objectives—such as minimizing charge time and thermal stress—based on user-defined preferences. Unlike scalar reward methods, MORL captures trade-offs explicitly and adapts charging behavior to context. Experimental results show the policy’s adaptability: faster charging is achieved when temperature constraints are relaxed, while more conservative profiles emerge when battery longevity is prioritized. This highlights the potential of MORL to enhance both the safety and efficiency of EV charging.
Red de Universidades con Carreras en Informática
description Fast and safe battery charging remains a critical barrier to large-scale EV adoption, as high charging rates accelerate battery degradation and increase thermal risk. Traditional control strategies often rely on static heuristics or single-objective optimization, limiting their ability to manage trade-offs between speed, safety, and battery longevity. This work proposes a deep multi-objective reinforcement learning (MORL) framework for optimal EV battery charging. The proposed agent learns policies that dynamically balance competing objectives—such as minimizing charge time and thermal stress—based on user-defined preferences. Unlike scalar reward methods, MORL captures trade-offs explicitly and adapts charging behavior to context. Experimental results show the policy’s adaptability: faster charging is achieved when temperature constraints are relaxed, while more conservative profiles emerge when battery longevity is prioritized. This highlights the potential of MORL to enhance both the safety and efficiency of EV charging.
publishDate 2025
dc.date.none.fl_str_mv 2025-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/191534
url http://sedici.unlp.edu.ar/handle/10915/191534
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-8258-99-7
info:eu-repo/semantics/reference/hdl/10915/189846
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
34-43
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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