Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

Author:

Adnane Marouane1ORCID,Khoumsi Ahmed1,Trovão João Pedro F.123ORCID

Affiliation:

1. e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada

2. Department of Electrical Engineering, Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, 3030-199 Coimbra, Portugal

3. Department of Electrical and Computer Engineering, INESC Coimbra, University of Coimbra, Polo II, 3030-290 Coimbra, Portugal

Abstract

Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives.

Funder

Canada Research Chairs Program

Natural Sciences and Engineering Research Council of Canada

FCT-Portuguese Foundation for Science and Technology

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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5. Sani, A.R., Hassan, M.U., and Chen, J. (2022). Privacy Preserving Machine Learning for Electric Vehicles: A Survey. arXiv.

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