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Machine learning for solving charging infrastructure planning problems : a comprehensive review

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Deb, Sanchari (2021) Machine learning for solving charging infrastructure planning problems : a comprehensive review. Energies, 14 (23). 7833. doi:10.3390/en14237833

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Official URL: https://doi.org/10.3390/en14237833

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Abstract

As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%. View Full-Text

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Other > Institute of Advanced Study
Library of Congress Subject Headings (LCSH): Electric vehicles , Battery charging stations (Electric vehicles) , Battery charging stations (Electric vehicles) -- Design and construction , Battery charging stations (Electric vehicles) -- Installation, Battery charging stations (Electric vehicles) -- Installation -- Data processing , Electric vehicles -- Power supply, Machine learning
Journal or Publication Title: Energies
Publisher: M.D.P.I.A.G.
ISSN: 1996-1073
Official Date: 23 November 2021
Dates:
DateEvent
23 November 2021Published
22 September 2021Accepted
Volume: 14
Number: 23
Article Number: 7833
DOI: 10.3390/en14237833
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDEuropean Research Consortium for Informatics and Mathematicshttp://dx.doi.org/10.13039/501100001667
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