An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

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dc.authoridCOLAK, ILHAMI/0000-0002-6405-5938
dc.authoridBLAIFI, Sid-ali/0000-0002-5503-5070
dc.authoridMERROUCHE, Walid/0000-0002-2498-7095
dc.contributor.authorBlaifi, S.
dc.contributor.authorMoulahoum, S.
dc.contributor.authorColak, I.
dc.contributor.authorMerrouche, W.
dc.date.accessioned2024-09-11T19:50:47Z
dc.date.available2024-09-11T19:50:47Z
dc.date.issued2016
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractModeling of batteries in photovoltaic systems has been a major issue related to the random dynamic regime imposed by the changes of solar irradiation and ambient temperature added to the complexity of battery electrochemical and electrical behaviors. However, various approaches have been proposed to model the battery behavior by predicting from detailed electrochemical, electrical or analytical models to high-level stochastic models. In this paper, an improvement of dynamic electrical battery model is proposed by automatic parameter extraction using genetic algorithm in order to give usefulness and future implementation for practical application. It is highlighted that the enhancement of 21 values of the parameters of CEIMAT model presents a good agreement with real measurements for different modes like charge or discharge and various conditions. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.apenergy.2016.02.062
dc.identifier.endpage898en_US
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118
dc.identifier.scopus2-s2.0-84959419261en_US
dc.identifier.startpage888en_US
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2016.02.062
dc.identifier.urihttps://hdl.handle.net/11363/7672
dc.identifier.volume169en_US
dc.identifier.wosWOS:000374196200071en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectPhotovoltaic Systems (PVS)en_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectLead-acid batteryen_US
dc.subjectStand-alone systemsen_US
dc.titleAn enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applicationsen_US
dc.typeArticleen_US

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