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

Author

Listed:
  • 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
    Department of Electrical Engineering, Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, 3030-199 Coimbra, Portugal
    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.

Suggested Citation

  • Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4897-:d:1177517
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    References listed on IDEAS

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