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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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    1. František Pollák & Josef Vodák & Jakub Soviar & Peter Markovič & Gianluca Lentini & Valerio Mazzeschi & Alessandro Luè, 2021. "Promotion of Electric Mobility in the European Union—Overview of Project PROMETEUS from the Perspective of Cohesion through Synergistic Cooperation on the Example of the Catching-Up Region," Sustainability, MDPI, vol. 13(3), pages 1-26, February.
    2. Gaete-Morales, Carlos & Kramer, Hendrik & Schill, Wolf-Peter, 2021. "An open tool for creating battery-electric vehicle time series from empirical data, emobpy," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 8.
    3. Cedric De Cauwer & Wouter Verbeke & Thierry Coosemans & Saphir Faid & Joeri Van Mierlo, 2017. "A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions," Energies, MDPI, vol. 10(5), pages 1-18, May.
    4. Tal, Gil & Karanam, Vaishnavi Chaitanya & Favetti, Matthew P. & Sutton, Katrina May & Ogunmayin, Jade Motayo & Raghavan, Seshadri Srinivasa & Nitta, Christopher & Chakraborty, Debapriya & Davis, Adam , 2021. "Emerging Technology Zero Emission Vehicle Household Travel and Refueling Behavior," Institute of Transportation Studies, Working Paper Series qt2v0853tp, Institute of Transportation Studies, UC Davis.
    5. Antonello Ignazio Croce & Giuseppe Musolino & Corrado Rindone & Antonino Vitetta, 2021. "Traffic and Energy Consumption Modelling of Electric Vehicles: Parameter Updating from Floating and Probe Vehicle Data," Energies, MDPI, vol. 15(1), pages 1-20, December.
    6. Johannes Ritzmann & Oscar Chinellato & Richard Hutter & Christopher Onder, 2021. "Optimal Integrated Emission Management through Variable Engine Calibration," Energies, MDPI, vol. 14(22), pages 1-23, November.
    7. Piotr Wróblewski & Jerzy Kupiec & Wojciech Drożdż & Wojciech Lewicki & Jarosław Jaworski, 2021. "The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions," Energies, MDPI, vol. 14(12), pages 1-17, June.
    8. Abdullah Dik & Siddig Omer & Rabah Boukhanouf, 2022. "Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration," Energies, MDPI, vol. 15(3), pages 1-26, January.
    9. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    10. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.
    11. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    12. Kamile Petrauskiene & Jolanta Dvarioniene & Giedrius Kaveckis & Daina Kliaugaite & Julie Chenadec & Leonie Hehn & Berta Pérez & Claudio Bordi & Giorgio Scavino & Andrea Vignoli & Michael Erman, 2020. "Situation Analysis of Policies for Electric Mobility Development: Experience from Five European Regions," Sustainability, MDPI, vol. 12(7), pages 1-21, April.
    13. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
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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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