Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control

The use of Model Predictive Control (MPC) for optimal charging of batteries is attracting attention due to its superiority over empirical charging protocols. But, the intricate nature of physics-based battery models poses a challenge to MPC implementation, necessitating substantial computational res...

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Autores: Shokry Abdelaleem Taha Zied, Ahmed, Abou El Qassime, Mehdi, Espuña Camarasa, Antonio|||0000-0002-1238-8108, Moulines, Eric François Victor
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/439418
Acceso en línea:https://hdl.handle.net/2117/439418
https://dx.doi.org/10.1016/j.compchemeng.2025.109096
Access Level:acceso abierto
Palabra clave:Lithium-ion batteries
Health-aware optimal charging
Model predictive control
Explicit control
Machine Learning
Deep neural networks
Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
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oai_identifier_str oai:upcommons.upc.edu:2117/439418
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repository_id_str
spelling Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive controlShokry Abdelaleem Taha Zied, AhmedAbou El Qassime, MehdiEspuña Camarasa, Antonio|||0000-0002-1238-8108Moulines, Eric François VictorLithium-ion batteriesHealth-aware optimal chargingModel predictive controlExplicit controlMachine LearningDeep neural networksÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdicaThe use of Model Predictive Control (MPC) for optimal charging of batteries is attracting attention due to its superiority over empirical charging protocols. But, the intricate nature of physics-based battery models poses a challenge to MPC implementation, necessitating substantial computational resources. Hence, this paper presents a method for explicit MPC based on machine learning (ML) models, applied for optimal battery charging while accounting for linear health constraints. The method uses Deep Neural Networks (DNNs) to construct offline control law that precisely describe the optimal charging current as a function of the battery's state. This DNN-based control law is developed using data generated by solving the MPC problem several times while varying the battery's initial state. Then, the control law is applied online to regulate the charging by cheaply predicting the closed-loop current. The method is numerically validated by its application to two case studies, showing: i) high accuracy in predicting closed-loop charging current (a normalized root mean square error of less than 1.0 %), ii) robustness in handling random initial states of the battery, iii) capability to learn bound and linear constraints directly from the data without any knowledge of their mathematical formulations, achieving a maximum constraint violation of an order of magnitude equal to 10-2, iv) applicability to distinct types of battery models, and v) a reduction in the required computational time compared to traditional MPC, which reaches up to 94.7%, in the lowest-performing testing scenario.Elsevier20252025-03-0920252025-07-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/439418https://dx.doi.org/10.1016/j.compchemeng.2025.109096reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4394182026-05-27T15:37:01Z
dc.title.none.fl_str_mv Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
title Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
spellingShingle Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
Shokry Abdelaleem Taha Zied, Ahmed
Lithium-ion batteries
Health-aware optimal charging
Model predictive control
Explicit control
Machine Learning
Deep neural networks
Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
title_short Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
title_full Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
title_fullStr Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
title_full_unstemmed Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
title_sort Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control
dc.creator.none.fl_str_mv Shokry Abdelaleem Taha Zied, Ahmed
Abou El Qassime, Mehdi
Espuña Camarasa, Antonio|||0000-0002-1238-8108
Moulines, Eric François Victor
author Shokry Abdelaleem Taha Zied, Ahmed
author_facet Shokry Abdelaleem Taha Zied, Ahmed
Abou El Qassime, Mehdi
Espuña Camarasa, Antonio|||0000-0002-1238-8108
Moulines, Eric François Victor
author_role author
author2 Abou El Qassime, Mehdi
Espuña Camarasa, Antonio|||0000-0002-1238-8108
Moulines, Eric François Victor
author2_role author
author
author
dc.subject.none.fl_str_mv Lithium-ion batteries
Health-aware optimal charging
Model predictive control
Explicit control
Machine Learning
Deep neural networks
Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
topic Lithium-ion batteries
Health-aware optimal charging
Model predictive control
Explicit control
Machine Learning
Deep neural networks
Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
description The use of Model Predictive Control (MPC) for optimal charging of batteries is attracting attention due to its superiority over empirical charging protocols. But, the intricate nature of physics-based battery models poses a challenge to MPC implementation, necessitating substantial computational resources. Hence, this paper presents a method for explicit MPC based on machine learning (ML) models, applied for optimal battery charging while accounting for linear health constraints. The method uses Deep Neural Networks (DNNs) to construct offline control law that precisely describe the optimal charging current as a function of the battery's state. This DNN-based control law is developed using data generated by solving the MPC problem several times while varying the battery's initial state. Then, the control law is applied online to regulate the charging by cheaply predicting the closed-loop current. The method is numerically validated by its application to two case studies, showing: i) high accuracy in predicting closed-loop charging current (a normalized root mean square error of less than 1.0 %), ii) robustness in handling random initial states of the battery, iii) capability to learn bound and linear constraints directly from the data without any knowledge of their mathematical formulations, achieving a maximum constraint violation of an order of magnitude equal to 10-2, iv) applicability to distinct types of battery models, and v) a reduction in the required computational time compared to traditional MPC, which reaches up to 94.7%, in the lowest-performing testing scenario.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-03-09
2025
2025-07-25
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://hdl.handle.net/2117/439418
https://dx.doi.org/10.1016/j.compchemeng.2025.109096
url https://hdl.handle.net/2117/439418
https://dx.doi.org/10.1016/j.compchemeng.2025.109096
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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