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A Versatile Model for Estimating the Fuel Consumption of a Wide Range of Transport Modes

Author

Listed:
  • Atiquzzaman Khan Ankur

    (Institute of Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany)

  • Stefan Kraus

    (Institute of Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
    Chair for Fuel Cells, RWTH Aachen University, c/o Institute of Techno-Economic Systems Analysis, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany)

  • Thomas Grube

    (Institute of Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany)

  • Rui Castro

    (INESC-ID/IST, University of Lisbon, 1000-029 Lisboa, Portugal)

  • Detlef Stolten

    (Institute of Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
    Chair for Fuel Cells, RWTH Aachen University, c/o Institute of Techno-Economic Systems Analysis, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany)

Abstract

The importance of a flexible and comprehensive vehicle fuel consumption model cannot be understated for understanding the implications of the modal changes currently occurring in the transportation sector. In this study, a model is developed to determine the tank-to-wheel energy demand for passenger and freight transportation within Germany for different modes of transport. These modes include light-duty vehicles (LDVs), heavy-duty vehicles (HDVs), airplanes, trains, ships, and unmanned aviation. The model further estimates future development through 2050. Utilizing standard driving cycles, backward-looking longitudinal vehicle models are employed to determine the energy demand for all on-road vehicle modes. For non-road vehicle modes, energy demand from the literature is drawn upon to develop the model. It is found that various vehicle parameters exert different effects on vehicle energy demand, depending on the driving scenario. Public transportation offers the most energy-efficient means of travel in the forms of battery electric buses (33.9 MJ/100 pkm), battery electric coaches (21.3 MJ/100 pkm), fuel cell electric coaches (32.9 MJ/100 pkm), trams (43.3 MJ/100 pkm), and long-distance electric trains (31.8 MJ/100 pkm). International shipping (9.9 MJ/100 tkm) is the most energy-efficient means of freight transport. The electrification of drivetrains and the implementation of regenerative braking show large potential for fuel consumption reduction, especially in urban areas. Occupancy and loading rates for vehicles play a critical role in determining the energy demand per passenger-kilometer for passenger modes, and tonne-kilometer for freight modes.

Suggested Citation

  • Atiquzzaman Khan Ankur & Stefan Kraus & Thomas Grube & Rui Castro & Detlef Stolten, 2022. "A Versatile Model for Estimating the Fuel Consumption of a Wide Range of Transport Modes," Energies, MDPI, vol. 15(6), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2232-:d:774275
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    References listed on IDEAS

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    1. Mustapha Harb & Yu Xiao & Giovanni Circella & Patricia L. Mokhtarian & Joan L. Walker, 2018. "Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment," Transportation, Springer, vol. 45(6), pages 1671-1685, November.
    2. Schimpe, Michael & Naumann, Maik & Truong, Nam & Hesse, Holger C. & Santhanagopalan, Shriram & Saxon, Aron & Jossen, Andreas, 2018. "Energy efficiency evaluation of a stationary lithium-ion battery container storage system via electro-thermal modeling and detailed component analysis," Applied Energy, Elsevier, vol. 210(C), pages 211-229.
    3. 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.
    4. Park, Yongha & O’Kelly, Morton E., 2014. "Fuel burn rates of commercial passenger aircraft: variations by seat configuration and stage distance," Journal of Transport Geography, Elsevier, vol. 41(C), pages 137-147.
    5. Thomas Grube & Detlef Stolten, 2018. "The Impact of Drive Cycles and Auxiliary Power on Passenger Car Fuel Economy," Energies, MDPI, vol. 11(4), pages 1-26, April.
    6. Polychronis Spanoudakis & Nikolaos C. Tsourveloudis & Lefteris Doitsidis & Emmanuel S. Karapidakis, 2019. "Experimental Research of Transmissions on Electric Vehicles’ Energy Consumption," Energies, MDPI, vol. 12(3), pages 1-15, January.
    7. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    8. Wang, Jinghui & Rakha, Hesham A., 2017. "Electric train energy consumption modeling," Applied Energy, Elsevier, vol. 193(C), pages 346-355.
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    Cited by:

    1. Tomasz Lech Stańczyk & Leon Prochowski & Damian Cegłowski & Emilia M. Szumska & Mateusz Ziubiński, 2023. "Assessment of Driver Performance and Energy Efficiency in Transportation Tasks when Vehicle Weight Undergoes Significant Changes," Energies, MDPI, vol. 16(15), pages 1-27, July.
    2. Stefan Tabacu & Dragos Popa, 2023. "Backward-Facing Analysis for the Preliminary Estimation of the Vehicle Fuel Consumption," Sustainability, MDPI, vol. 15(6), pages 1-19, March.

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