Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter

This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the...

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Autores: Fahmy, Hend M., Swief, Rania A., Hasanien, Hany M., Alharbi, Mohammed, Maldonado-Ortega, José Luis, Jurado-Melguizo, Francisco
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2023
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/2939
Acceso en línea:https://www.mdpi.com/1996-1073/16/14/5558
https://hdl.handle.net/10953/2939
Access Level:acceso abierto
Palabra clave:Li-ion batteries
Battery management system (BMS)
State of Charge (SoC)
Battery model
Parameter identification
Kalman filters
Coulomb counting method (CCM)
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spelling Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman FilterFahmy, Hend M.Swief, Rania A.Hasanien, Hany M.Alharbi, MohammedMaldonado-Ortega, José LuisJurado-Melguizo, FranciscoLi-ion batteriesBattery management system (BMS)State of Charge (SoC)Battery modelParameter identificationKalman filtersCoulomb counting method (CCM)This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.MDPI202420242023info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://www.mdpi.com/1996-1073/16/14/5558https://hdl.handle.net/10953/2939reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésEnergies [2023]; [16]: [5558]Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/29392026-06-24T12:41:07Z
dc.title.none.fl_str_mv Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
title Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
spellingShingle Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
Fahmy, Hend M.
Li-ion batteries
Battery management system (BMS)
State of Charge (SoC)
Battery model
Parameter identification
Kalman filters
Coulomb counting method (CCM)
title_short Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
title_full Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
title_fullStr Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
title_full_unstemmed Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
title_sort Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
dc.creator.none.fl_str_mv Fahmy, Hend M.
Swief, Rania A.
Hasanien, Hany M.
Alharbi, Mohammed
Maldonado-Ortega, José Luis
Jurado-Melguizo, Francisco
author Fahmy, Hend M.
author_facet Fahmy, Hend M.
Swief, Rania A.
Hasanien, Hany M.
Alharbi, Mohammed
Maldonado-Ortega, José Luis
Jurado-Melguizo, Francisco
author_role author
author2 Swief, Rania A.
Hasanien, Hany M.
Alharbi, Mohammed
Maldonado-Ortega, José Luis
Jurado-Melguizo, Francisco
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Li-ion batteries
Battery management system (BMS)
State of Charge (SoC)
Battery model
Parameter identification
Kalman filters
Coulomb counting method (CCM)
topic Li-ion batteries
Battery management system (BMS)
State of Charge (SoC)
Battery model
Parameter identification
Kalman filters
Coulomb counting method (CCM)
description This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://www.mdpi.com/1996-1073/16/14/5558
https://hdl.handle.net/10953/2939
url https://www.mdpi.com/1996-1073/16/14/5558
https://hdl.handle.net/10953/2939
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Energies [2023]; [16]: [5558]
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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