Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/10892
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fescioğlu Ünver, Nilgün | - |
dc.contributor.author | Yıldız, Aktaş, M. | - |
dc.date.accessioned | 2023-12-23T06:07:19Z | - |
dc.date.available | 2023-12-23T06:07:19Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 1364-0321 | - |
dc.identifier.uri | https://doi.org/10.1016/j.rser.2023.113873 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/10892 | - |
dc.description.abstract | The 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 Ltd | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.relation.ispartof | Renewable and Sustainable Energy Reviews | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Charge scheduling | en_US |
dc.subject | Charging service operations | en_US |
dc.subject | Electric vehicle | en_US |
dc.subject | Infrastructure planning | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Pricing | en_US |
dc.subject | Routing | en_US |
dc.subject | Charging (batteries) | en_US |
dc.subject | Electric vehicles | en_US |
dc.subject | Learning algorithms | en_US |
dc.subject | Reinforcement learning | en_US |
dc.subject | Vehicle routing | en_US |
dc.subject | Charge scheduling | en_US |
dc.subject | Charging service operation | en_US |
dc.subject | Electric vehicle charging | en_US |
dc.subject | Infrastructure planning | en_US |
dc.subject | Machine learning applications | en_US |
dc.subject | Machine-learning | en_US |
dc.subject | Planning controls | en_US |
dc.subject | Road transportation | en_US |
dc.subject | Routings | en_US |
dc.subject | Service operations | en_US |
dc.subject | Costs | en_US |
dc.title | Electric Vehicle Charging Service Operations: a Review of Machine Learning Applications for Infrastructure Planning, Control, Pricing and Routing | en_US |
dc.type | Review | en_US |
dc.department | TOBB ETÜ | en_US |
dc.identifier.volume | 188 | en_US |
dc.identifier.wos | WOS:001159387000001 | en_US |
dc.identifier.scopus | 2-s2.0-85174214542 | en_US |
dc.institutionauthor | … | - |
dc.identifier.doi | 10.1016/j.rser.2023.113873 | - |
dc.authorscopusid | 9639266600 | - |
dc.authorscopusid | 57224221538 | - |
dc.relation.publicationcategory | Diğer | en_US |
item.openairetype | Review | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.4. 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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