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A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles

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  • Anil K. Madhusudhanan

    (Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK)

  • Xiaoxiang Na

    (Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK)

  • David Cebon

    (Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK)

Abstract

This article proposes a novel framework to develop computationally efficient energy consumption models of electric and internal combustion engine vehicles. The number of calculations in a conventional energy consumption model prevents the model’s usage in applications where time is limited. As many fleet operators around the world are in the process of transitioning towards electric vehicles, a computationally efficient energy consumption model will be valuable to analyse the vehicles they trial. A vehicle’s energy consumption depends on the vehicle characteristics, drive cycles and vehicle mass. The proposed modelling framework considers these aspects, is computationally efficient, and can be run using open source software packages. The framework is validated through two use cases: an electric bus and a diesel truck. The model error’s standard deviation is less 5% and its mean is less than 2%. The proposed model’s mean computation time is less than 20 ms, which is two orders of magnitude lower than that of the baseline model. Finally, a case study was performed to illustrate the usefulness of the modelling framework for a fleet operator.

Suggested Citation

  • Anil K. Madhusudhanan & Xiaoxiang Na & David Cebon, 2021. "A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles," Energies, MDPI, vol. 14(7), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:2031-:d:531154
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    References listed on IDEAS

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    1. Briggs, Ian & Murtagh, Martin & Kee, Robert & McCulloug, Geoffrey & Douglas, Roy, 2017. "Sustainable non-automotive vehicles: The simulation challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 840-851.
    2. Turkensteen, Marcel, 2017. "The accuracy of carbon emission and fuel consumption computations in green vehicle routing," European Journal of Operational Research, Elsevier, vol. 262(2), pages 647-659.
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    Cited by:

    1. Abood Mourad & Martin Hennebel & Ahmed Amrani & Amira Ben Hamida, 2021. "Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network," Energies, MDPI, vol. 14(9), pages 1-20, April.
    2. Bray, Garrett & Cebon, David, 2022. "Selection of vehicle size and extent of multi-drop deliveries for autonomous goods vehicles: An assessment of potential for change," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    3. Bray, Garrett & Cebon, David, 2022. "Operational speed strategy opportunities for autonomous trucking on highways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 75-94.
    4. Neil Stephen Lopez & Adrian Allana & Jose Bienvenido Manuel Biona, 2021. "Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model," Energies, MDPI, vol. 14(13), pages 1-15, June.
    5. Binbin Sun & Tianqi Gu & Mengxue Xie & Pengwei Wang & Song Gao & Xi Zhang, 2022. "Strategy Design and Performance Analysis of an Electromechanical Flywheel Hybrid Scheme for Electric Vehicles," Sustainability, MDPI, vol. 14(17), pages 1-17, September.

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