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Title	Machine Learning For Solving Charging Infrastructure Planning Problems: A Comprehensive Review
ID_Doc	35983
Authors	Deb S.
Year	2021
Published	Energies, 14, 23
DOI	http://dx.doi.org/10.3390/en14237833
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%. © 2021 by the author. Licensee MDPI, Basel, Switzerland.
Author Keywords	Charging; Electric vehicle; Machine learning; Review

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