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dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)spa
dc.contributor.authorSyah, Rahmad
dc.contributor.authorGrimaldo Guerrero, John William
dc.contributor.authorLeonidovich Poltarykhin, Andrey
dc.contributor.authorSuksatan, Wanich
dc.contributor.authorRavindhan, Surendar
dc.contributor.authorBokov, Dmitry O.
dc.contributor.authorAbdelbasset, Walid Kamal
dc.contributor.authorAl-Janabi, Samaher
dc.contributor.authorAlkaim, Ayad F.
dc.contributor.authorYu. Tumanovj, Dmitriy
dc.date.accessioned2022-09-30T00:48:45Z
dc.date.available2022-09-30T00:48:45Z
dc.date.issued2022
dc.identifier.citationahmad Syah, John William Grimaldo Guerrero, Andrey Leonidovich Poltarykhin, Wanich Suksatan, Surendar Aravindhan, Dmitry O. Bokov, Walid Kamal Abdelbasset, Samaher Al-Janabi, Ayad F. Alkaim, Dmitriy Yu. Tumanov, Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell, Energy Reports, Volume 8, 2022, Pages 10776-10785, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2022.08.177.spa
dc.identifier.urihttps://hdl.handle.net/11323/9549
dc.description.abstractThis paper proposes a new optimal methodology for model parameters estimation of the Proton Exchange Membrane Fuel Cell. The main purpose here is to design a newly developed metaheuristic technique to deliver a model with higher accuracy. In this study, we utilized two modifications for the Teamwork Optimizer to get higher accuracy. The two modifiers are opposition-based learning and chaotic mechanism. The results show that using the opposition-based learning, the population diversity has been kept, owing to the greater population size due to the solution space, and using the Chaos theory, the population diversity has been increased. This is proved by applying the Improved Teamwork Optimizer to minimize the Root Mean Square Error and Integral Absolute Error between the suggested model and empirical data. The validation has been done by applying the proposed Improved Teamwork Optimizer to two studied cases, which are Nexa Proton Exchange Membrane Fuel Cell and NedSstack PS6 Proton Exchange Membrane Fuel Cell, and comparing it with other published works. Simulation results showed that the proposed method with 1.14 Integral Absolute Error and 0.21 Root Mean Square Error for NedSstack PS6 Proton Exchange Membrane Fuel Cells and with 12 Integral Absolute Error and 0.17 Root Mean Square Error for Nexa Proton Exchange Membrane Fuel Cells provides the minimum error value among the other optimization techniques. This shows the higher potential of the proposed method for use as the parameter estimator for Proton Exchange Membrane Fuel Cells.eng
dc.format.extent10 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherElsevier Ltd.spa
dc.rights© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S2352484722016225?via%3Dihubspa
dc.titleDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel celleng
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doi10.1016/j.egyr.2022.08.177
dc.identifier.eissn2352-4847spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.placeUnited Kingdomspa
dc.relation.ispartofjournalEnergy Reportsspa
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dc.subject.proposalSystem estimationeng
dc.subject.proposalPEMFCeng
dc.subject.proposalImproved Teamwork Optimizereng
dc.subject.proposalVoltage profileeng
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dc.relation.citationendpage10785spa
dc.relation.citationstartpage10776spa
dc.relation.citationvolume8spa
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dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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