Data-driven MPPT techniques for optimizing vehicular fuel cell performance in hybrid DC microgrid

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dc.authoridCELIK, Ozgur/0000-0002-7683-2415
dc.contributor.authorCelik, Ozgur
dc.date.accessioned2025-01-06T17:38:04Z
dc.date.available2025-01-06T17:38:04Z
dc.date.issued2024
dc.description.abstractThis paper aims to apply data-driven maximum power point tracking (MPPT) techniques specifically tailored for fuel cell vehicle (FCV) supported hybrid DC microgrids to enhance the power harvesting capability of fuel cell (FC) stacks. Compared to existing MPPT techniques, the current study focuses on developing and evaluating data- driven approaches for maximum power extraction by dynamically determining the operating point of FC power sources through a Zeta converter. An in-depth analysis is conducted by considering parameters such as efficiency, tracking accuracy, response time, and robustness to variations in load demand and operating conditions. The performance results validate that the developed three-layer neural network (TNN)-based MPPT gives better performance findings than Gaussian process regression (GPR), support vector regression (SVR), decision tree regression (DTR), and bagging ensemble decision tree (BEDT). In the performance evaluation phase, a vehicular FC with a rating of 1.26 kW is designed and operated within the temperature range of 320 K to 343 K for hydrogen pressure values ranging from 1 bar to 1.8 bar. For these operational conditions, the prediction accuracy value of the proposed TNN method is 99.6% while the performance values GPR, SVR, DTR, and BEDT are 99%, 98.6%, 97.2%, and 96%. In addition, system efficiency is increased by 0.98%, 1.25%, 2.51%, and 3.02% compared to GPR, SVR, DTR, and BEDT, respectively.
dc.identifier.doi10.1016/j.ijhydene.2024.07.033
dc.identifier.endpage727
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.scopus2-s2.0-85198006882
dc.identifier.scopusqualityQ1
dc.identifier.startpage715
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2024.07.033
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2458
dc.identifier.volume79
dc.identifier.wosWOS:001333917600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Hydrogen Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFuel cell
dc.subjectVehicle
dc.subjectData-driven
dc.subjectMPPT
dc.subjectPower harvesting
dc.titleData-driven MPPT techniques for optimizing vehicular fuel cell performance in hybrid DC microgrid
dc.typeArticle

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