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Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach

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  • Hamza Mediouni

    (Energy Optimization, Diagnosis and Control Team, Center for Research in Engineering and Health Sciences and Techniques, ENSAM, Mohammed V University, Rabat 10100, Morocco
    TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11100, Morocco)

  • Amal Ezzouhri

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11100, Morocco
    ERSC Team, Mohammadia Engineering School, Mohammed V University, Rabat 10090, Morocco)

  • Zakaria Charouh

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11100, Morocco
    ERSC Team, Mohammadia Engineering School, Mohammed V University, Rabat 10090, Morocco)

  • Khadija El Harouri

    (Energy Optimization, Diagnosis and Control Team, Center for Research in Engineering and Health Sciences and Techniques, ENSAM, Mohammed V University, Rabat 10100, Morocco)

  • Soumia El Hani

    (Energy Optimization, Diagnosis and Control Team, Center for Research in Engineering and Health Sciences and Techniques, ENSAM, Mohammed V University, Rabat 10100, Morocco)

  • Mounir Ghogho

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11100, Morocco
    School of EEE, University of Leeds, Leeds LS2 9JT, UK)

Abstract

Range anxiety remains one of the main hurdles to the widespread adoption of electric vehicles (EVs). To mitigate this issue, accurate energy consumption prediction is required. In this study, a hybrid approach is proposed toward this objective by taking into account driving behavior, road conditions, natural environment, and additional weight. The main components of the EV were simulated using physical and equation-based models. A rich synthetic dataset illustrating different driving scenarios was then constructed. Real-world data were also collected using a city car. A machine learning model was built to relate the mechanical power to the electric power. The proposed predictive method achieved an R 2 of 0.99 on test synthetic data and an R 2 of 0.98 on real-world data. Furthermore, the instantaneous regenerative braking power efficiency as a function of the deceleration level was also investigated in this study.

Suggested Citation

  • Hamza Mediouni & Amal Ezzouhri & Zakaria Charouh & Khadija El Harouri & Soumia El Hani & Mounir Ghogho, 2022. "Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach," Energies, MDPI, vol. 15(17), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6490-:d:907464
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    References listed on IDEAS

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    Cited by:

    1. Vasyl Mateichyk & Nataliia Kostian & Miroslaw Smieszek & Igor Gritsuk & Valerii Verbovskyi, 2023. "Review of Methods for Evaluating the Energy Efficiency of Vehicles with Conventional and Alternative Power Plants," Energies, MDPI, vol. 16(17), pages 1-25, August.
    2. Klemen Deželak & Klemen Sredenšek & Sebastijan Seme, 2023. "Energy Consumption and Grid Interaction Analysis of Electric Vehicles Based on Particle Swarm Optimisation Method," Energies, MDPI, vol. 16(14), pages 1-15, July.
    3. Maksymilian Mądziel & Tiziana Campisi, 2023. "Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database," Energies, MDPI, vol. 16(3), pages 1-18, February.

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