Battery state-of-health estimation: a step towards battery digital twins

For a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a nove...

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Detalles Bibliográficos
Autores: Safavi, Vahid, Bazmohammadi, Najmeh, Vasquez Quintero, Juan Carlos, Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
Tipo de recurso: artículo
Fecha de publicación:2024
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:2117/409053
Acceso en línea:https://hdl.handle.net/2117/409053
https://dx.doi.org/10.3390/electronics13030587
Access Level:acceso abierto
Palabra clave:Lithium ion batteries
Lithium-ion batteries
State of health
Data pre-processing
Discharging characteristics
Digital twin
Deep learning
CNN-LSTM
Bateries d'ió liti
Aprenentatge profund
Àrees temàtiques de la UPC::Enginyeria electrònica
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oai_identifier_str oai:upcommons.upc.edu:2117/409053
network_acronym_str ES
network_name_str España
repository_id_str
spelling Battery state-of-health estimation: a step towards battery digital twinsSafavi, VahidBazmohammadi, NajmehVasquez Quintero, Juan CarlosGuerrero Zapata, Josep Maria|||0000-0001-5236-4592Lithium ion batteriesLithium-ion batteriesState of healthData pre-processingDischarging characteristicsDigital twinDeep learningCNN-LSTMBateries d'ió litiAprenentatge profundÀrees temàtiques de la UPC::Enginyeria electrònicaFor a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a novel data pre-processing model is proposed in this paper to extract health-related features automatically from battery-discharging data for SOH estimation. In the proposed method, one-dimensional (1D) voltage data are converted to two-dimensional (2D) data, and a new data set is created using a 2D sliding window. Then, features are automatically extracted in the machine learning (ML) training process. Finally, the estimation of the SOH is achieved by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated on the NASA public data set for a Li-ion battery degradation analysis in four different scenarios. The simulation results show a considerable reduction in the RMSE of battery SOH estimation. The proposed method eliminates the need for the manual extraction and evaluation of features, which is an important step toward automating the SOH estimation process and developing battery digital twins.Peer Reviewed20242024-01-3120242024-05-30journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/409053https://dx.doi.org/10.3390/electronics13030587reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4090532026-05-27T15:37:01Z
dc.title.none.fl_str_mv Battery state-of-health estimation: a step towards battery digital twins
title Battery state-of-health estimation: a step towards battery digital twins
spellingShingle Battery state-of-health estimation: a step towards battery digital twins
Safavi, Vahid
Lithium ion batteries
Lithium-ion batteries
State of health
Data pre-processing
Discharging characteristics
Digital twin
Deep learning
CNN-LSTM
Bateries d'ió liti
Aprenentatge profund
Àrees temàtiques de la UPC::Enginyeria electrònica
title_short Battery state-of-health estimation: a step towards battery digital twins
title_full Battery state-of-health estimation: a step towards battery digital twins
title_fullStr Battery state-of-health estimation: a step towards battery digital twins
title_full_unstemmed Battery state-of-health estimation: a step towards battery digital twins
title_sort Battery state-of-health estimation: a step towards battery digital twins
dc.creator.none.fl_str_mv Safavi, Vahid
Bazmohammadi, Najmeh
Vasquez Quintero, Juan Carlos
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author Safavi, Vahid
author_facet Safavi, Vahid
Bazmohammadi, Najmeh
Vasquez Quintero, Juan Carlos
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author_role author
author2 Bazmohammadi, Najmeh
Vasquez Quintero, Juan Carlos
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author2_role author
author
author
dc.subject.none.fl_str_mv Lithium ion batteries
Lithium-ion batteries
State of health
Data pre-processing
Discharging characteristics
Digital twin
Deep learning
CNN-LSTM
Bateries d'ió liti
Aprenentatge profund
Àrees temàtiques de la UPC::Enginyeria electrònica
topic Lithium ion batteries
Lithium-ion batteries
State of health
Data pre-processing
Discharging characteristics
Digital twin
Deep learning
CNN-LSTM
Bateries d'ió liti
Aprenentatge profund
Àrees temàtiques de la UPC::Enginyeria electrònica
description For a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a novel data pre-processing model is proposed in this paper to extract health-related features automatically from battery-discharging data for SOH estimation. In the proposed method, one-dimensional (1D) voltage data are converted to two-dimensional (2D) data, and a new data set is created using a 2D sliding window. Then, features are automatically extracted in the machine learning (ML) training process. Finally, the estimation of the SOH is achieved by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated on the NASA public data set for a Li-ion battery degradation analysis in four different scenarios. The simulation results show a considerable reduction in the RMSE of battery SOH estimation. The proposed method eliminates the need for the manual extraction and evaluation of features, which is an important step toward automating the SOH estimation process and developing battery digital twins.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-31
2024
2024-05-30
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/2117/409053
https://dx.doi.org/10.3390/electronics13030587
url https://hdl.handle.net/2117/409053
https://dx.doi.org/10.3390/electronics13030587
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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 15.228081

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