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

dc.contributor.authorFahmy, Hend M.
dc.contributor.authorSwief, Rania A.
dc.contributor.authorHasanien, Hany M.
dc.contributor.authorAlharbi, Mohammed
dc.contributor.authorMaldonado-Ortega, José Luis
dc.contributor.authorJurado-Melguizo, Francisco
dc.date.accessioned2024-06-25T10:56:33Z
dc.date.available2024-06-25T10:56:33Z
dc.date.issued2023-07
dc.description.abstractThis 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.es_ES
dc.identifier.citationFahmy, H.M.; Swief, R.A.; Hasanien, H.M.; Alharbi, M.; Maldonado, J.L.; Jurado, F. Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter. Energies 2023, 16, 5558. https://doi.org/10.3390/en16145558es_ES
dc.identifier.issn1996-1073es_ES
dc.identifier.other10.3390/en16145558es_ES
dc.identifier.urihttps://www.mdpi.com/1996-1073/16/14/5558es_ES
dc.identifier.urihttps://hdl.handle.net/10953/2939
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.ispartofEnergies [2023]; [16]: [5558]es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectLi-ion batterieses_ES
dc.subjectBattery management system (BMS)es_ES
dc.subjectState of Charge (SoC)es_ES
dc.subjectBattery modeles_ES
dc.subjectParameter identificationes_ES
dc.subjectKalman filterses_ES
dc.subjectCoulomb counting method (CCM)es_ES
dc.titleHybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filteres_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES

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