Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10892
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dc.contributor.authorFescioğlu Ünver, Nilgün-
dc.contributor.authorYıldız, Aktaş, M.-
dc.date.accessioned2023-12-23T06:07:19Z-
dc.date.available2023-12-23T06:07:19Z-
dc.date.issued2023-
dc.identifier.issn1364-0321-
dc.identifier.urihttps://doi.org/10.1016/j.rser.2023.113873-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10892-
dc.description.abstractThe majority of global road transportation emissions come from passenger and freight vehicles. Electric vehicles (EV) provide a sustainable transportation way, but customers’ charging service related concerns affect the EV adoption rate. Effective infrastructure planning, charge scheduling, charge pricing, and electric vehicle routing strategies can help relieve customer perceived risks. The number of studies using machine learning algorithms to solve these problems is increasing daily. Forecasting, clustering, and reinforcement based models are frequently the main solution methods or provide valuable inputs to other solution procedures. This study reviews the studies that apply machine learning models to improve EV charging service operations and provides future research directions. © 2023 Elsevier Ltden_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofRenewable and Sustainable Energy Reviewsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCharge schedulingen_US
dc.subjectCharging service operationsen_US
dc.subjectElectric vehicleen_US
dc.subjectInfrastructure planningen_US
dc.subjectMachine learningen_US
dc.subjectPricingen_US
dc.subjectRoutingen_US
dc.subjectCharging (batteries)en_US
dc.subjectElectric vehiclesen_US
dc.subjectLearning algorithmsen_US
dc.subjectReinforcement learningen_US
dc.subjectVehicle routingen_US
dc.subjectCharge schedulingen_US
dc.subjectCharging service operationen_US
dc.subjectElectric vehicle chargingen_US
dc.subjectInfrastructure planningen_US
dc.subjectMachine learning applicationsen_US
dc.subjectMachine-learningen_US
dc.subjectPlanning controlsen_US
dc.subjectRoad transportationen_US
dc.subjectRoutingsen_US
dc.subjectService operationsen_US
dc.subjectCostsen_US
dc.titleElectric Vehicle Charging Service Operations: a Review of Machine Learning Applications for Infrastructure Planning, Control, Pricing and Routingen_US
dc.typeReviewen_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume188en_US
dc.identifier.wosWOS:001159387000001en_US
dc.identifier.scopus2-s2.0-85174214542en_US
dc.institutionauthor-
dc.identifier.doi10.1016/j.rser.2023.113873-
dc.authorscopusid9639266600-
dc.authorscopusid57224221538-
dc.relation.publicationcategoryDiğeren_US
item.openairetypeReview-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.4. Department of Industrial Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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