Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review

Sanchari Deb

doi:10.1109/ICSGSC52434.2021.9490407

Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review

Sanchari Deb

VTT Technical Research Centre of Finland

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

9 Citations (Scopus)

Abstract

The ever-growing energy demand accompanied with environmental pollution has initiated a paradigm shift towards Electric Vehicles (EVs) from conventional vehicles. Public acceptance of EVs call for availability of charging infrastructure. Charging infrastructure planning is an intricate process involving various activities such as charging station placement, charging demand prediction, charging scheduling etc and interaction of power distribution as well as road network. In recent years, the advent of machine learning has made data driven approaches popular for solving charging infrastructure planning problem. Consequently, researchers have started using machine learning techniques for solving problems associated with charging infrastructure planning such as charging station placement, charging demand prediction, charging scheduling etc. This work aims to provide a comprehensive review of machine learning applications for solving charging infrastructure planning.

Original language	English
Title of host publication	5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021
Editors	Om Malik, Liansong Xiong
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	16-22
ISBN (Electronic)	978-1-6654-0134-0
ISBN (Print)	978-1-6654-2970-2
DOIs	https://doi.org/10.1109/ICSGSC52434.2021.9490407
Publication status	Published - 18 Jun 2021
MoE publication type	A4 Article in a conference publication
Event	5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021 - Tokyo, Japan Duration: 18 Jun 2021 → 20 Jun 2021

Publication series

Series	International Conference on Smart Grid and Smart Cities (ICSGSC)
Volume	5
ISSN	2768-007X

Conference

Conference	5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021
Abbreviated title	ICSGSC 2021
Country/Territory	Japan
City	Tokyo
Period	18/06/21 → 20/06/21

Funding

ACONK EDL GEMENT This research is supported by European Consortium of Informatics and Mathematics (ERCIM)

Keywords

charger
electric vehicle
machine learning
review

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICSGSC52434.2021.9490407

Cite this

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title = "Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review",

abstract = "The ever-growing energy demand accompanied with environmental pollution has initiated a paradigm shift towards Electric Vehicles (EVs) from conventional vehicles. Public acceptance of EVs call for availability of charging infrastructure. Charging infrastructure planning is an intricate process involving various activities such as charging station placement, charging demand prediction, charging scheduling etc and interaction of power distribution as well as road network. In recent years, the advent of machine learning has made data driven approaches popular for solving charging infrastructure planning problem. Consequently, researchers have started using machine learning techniques for solving problems associated with charging infrastructure planning such as charging station placement, charging demand prediction, charging scheduling etc. This work aims to provide a comprehensive review of machine learning applications for solving charging infrastructure planning.",

keywords = "charger, electric vehicle, machine learning, review",

author = "Sanchari Deb",

note = "Funding Information: ACONK EDL GEMENT This research is supported by European Consortium of Informatics and Mathematics (ERCIM) Publisher Copyright: {\textcopyright} 2021 IEEE.; 5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021, ICSGSC 2021 ; Conference date: 18-06-2021 Through 20-06-2021",

year = "2021",

month = jun,

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doi = "10.1109/ICSGSC52434.2021.9490407",

language = "English",

isbn = "978-1-6654-2970-2",

series = "International Conference on Smart Grid and Smart Cities (ICSGSC)",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

pages = "16--22",

editor = "Om Malik and Liansong Xiong",

booktitle = "5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021",

address = "United States",

}

Deb, S 2021, Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. in O Malik & L Xiong (eds), 5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021., 9490407, IEEE Institute of Electrical and Electronic Engineers, International Conference on Smart Grid and Smart Cities (ICSGSC), vol. 5, pp. 16-22, 5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021, Tokyo, Japan, 18/06/21. https://doi.org/10.1109/ICSGSC52434.2021.9490407

Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. / Deb, Sanchari.
5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021. ed. / Om Malik; Liansong Xiong. IEEE Institute of Electrical and Electronic Engineers, 2021. p. 16-22 9490407 (International Conference on Smart Grid and Smart Cities (ICSGSC), Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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N2 - The ever-growing energy demand accompanied with environmental pollution has initiated a paradigm shift towards Electric Vehicles (EVs) from conventional vehicles. Public acceptance of EVs call for availability of charging infrastructure. Charging infrastructure planning is an intricate process involving various activities such as charging station placement, charging demand prediction, charging scheduling etc and interaction of power distribution as well as road network. In recent years, the advent of machine learning has made data driven approaches popular for solving charging infrastructure planning problem. Consequently, researchers have started using machine learning techniques for solving problems associated with charging infrastructure planning such as charging station placement, charging demand prediction, charging scheduling etc. This work aims to provide a comprehensive review of machine learning applications for solving charging infrastructure planning.

AB - The ever-growing energy demand accompanied with environmental pollution has initiated a paradigm shift towards Electric Vehicles (EVs) from conventional vehicles. Public acceptance of EVs call for availability of charging infrastructure. Charging infrastructure planning is an intricate process involving various activities such as charging station placement, charging demand prediction, charging scheduling etc and interaction of power distribution as well as road network. In recent years, the advent of machine learning has made data driven approaches popular for solving charging infrastructure planning problem. Consequently, researchers have started using machine learning techniques for solving problems associated with charging infrastructure planning such as charging station placement, charging demand prediction, charging scheduling etc. This work aims to provide a comprehensive review of machine learning applications for solving charging infrastructure planning.

KW - charger

KW - electric vehicle

KW - machine learning

KW - review

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Deb S. Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In Malik O, Xiong L, editors, 5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021. IEEE Institute of Electrical and Electronic Engineers. 2021. p. 16-22. 9490407. (International Conference on Smart Grid and Smart Cities (ICSGSC), Vol. 5). doi: 10.1109/ICSGSC52434.2021.9490407

Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

Access to Document

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