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

Marouane Adnane, Ahmed Khoumsi and João Pedro F. Trovão ()
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Marouane Adnane: e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Ahmed Khoumsi: e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
João Pedro F. Trovão: e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada

Energies, 2023, vol. 16, issue 13, 1-39

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.

Keywords: electric vehicle; machine learning; energy consumption; systematic mapping study; energy management strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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