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Energy management strategies, control systems, and artificial intelligence-based algorithms development for hydrogen fuel cell-powered vehicles: A review

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dc.citedby51
dc.contributor.authorOladosu T.L.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorKoh S.P.J.en_US
dc.contributor.authorYusaf T.en_US
dc.contributor.authorid57202498005en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid57216824752en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid23112065900en_US
dc.date.accessioned2025-03-03T07:43:11Z
dc.date.available2025-03-03T07:43:11Z
dc.date.issued2024
dc.description.abstractHydrogen fuel cell electric vehicles (HFCEVs) are gaining revived attention due to the HFCEVs promising potential as important syndicates to net zero carbon emission attainment. However, HFCEVs' performance and cost-effectiveness do not yet match up with battery electric vehicles (BEVs) and traditional fossil fuel vehicles despite many different Energy Management System (EMS) strategies previously adopted. Rule-based controls are still limited specifically in handling multi-objective systems as HFCEVs and some optimization-based algorithms also pose computational and retrofitting difficulties. Therefore, this study presents the prospect of artificial intelligence-based algorithms, control systems, and energy management strategies advances on HFCEVs performance optimization. EMS strategies; AI-based algorithms categories, functions and hybridization; the state-of-art and future direction of AI-based algorithms and HFCEVs? cost components amongst others are explained in the study. The multi-objective-based algorithm, reinforcement learning algorithm, and different hybridizations are enhancing HFCEVs cost-competing edge. ? 2024 Hydrogen Energy Publications LLCen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.ijhydene.2024.02.284
dc.identifier.epage1404
dc.identifier.scopus2-s2.0-85187227450
dc.identifier.spage1380
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85187227450&doi=10.1016%2fj.ijhydene.2024.02.284&partnerID=40&md5=ceec9c46ac0c287e84a68ef976f20875
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36578
dc.identifier.volume61
dc.pagecount24
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Hydrogen Energy
dc.subjectControl systems
dc.subjectCost effectiveness
dc.subjectEnergy management
dc.subjectEnergy management systems
dc.subjectFossil fuels
dc.subjectHydrogen fuels
dc.subjectReinforcement learning
dc.subjectAlgorithms development
dc.subjectCarbon emissions
dc.subjectFuel cell electric vehicle
dc.subjectFuel-cell powered vehicles
dc.subjectHybridisation
dc.subjectHydrogen fuel cells
dc.subjectManagement strategies
dc.subjectMulti objective
dc.subjectSystem strategies
dc.subjectZero carbons
dc.subjectFuel cells
dc.titleEnergy management strategies, control systems, and artificial intelligence-based algorithms development for hydrogen fuel cell-powered vehicles: A reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
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