IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2232-d774275.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2232/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2232/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    4. Wang, Jinghui & Rakha, Hesham A., 2017. "Electric train energy consumption modeling," Applied Energy, Elsevier, vol. 193(C), pages 346-355.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    2. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    3. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).
    4. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    5. Sun, Xilei & Zhou, Feng & Fu, Jianqin & Liu, Jingping, 2024. "Experiment and simulation study on energy flow characteristics of a battery electric vehicle throughout the entire driving range in low-temperature conditions," Energy, Elsevier, vol. 292(C).
    6. Sun, Xilei & Fu, Jianqin, 2024. "Experiment investigation for interconnected effects of driving cycle and ambient temperature on bidirectional energy flows in an electric sport utility vehicle," Energy, Elsevier, vol. 300(C).
    7. Ibrahim, Amier & Jiang, Fangming, 2021. "The electric vehicle energy management: An overview of the energy system and related modeling and simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. David Watling & Patrícia Baptista & Gonçalo Duarte & Jianbing Gao & Haibo Chen, 2022. "Systematic Method for Developing Reference Driving Cycles Appropriate to Electric L-Category Vehicles," Energies, MDPI, vol. 15(9), pages 1-28, May.
    9. Oke, Jimi B. & Akkinepally, Arun Prakash & Chen, Siyu & Xie, Yifei & Aboutaleb, Youssef M. & Azevedo, Carlos Lima & Zegras, P. Christopher & Ferreira, Joseph & Ben-Akiva, Moshe, 2020. "Evaluating the systemic effects of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 98-126.
    10. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    11. K. S. Reddy & S. Aravindhan & Tapas K. Mallick, 2017. "Techno-Economic Investigation of Solar Powered Electric Auto-Rickshaw for a Sustainable Transport System," Energies, MDPI, vol. 10(6), pages 1-15, May.
    12. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    13. Stefano De Pinto & Pablo Camocardi & Christoforos Chatzikomis & Aldo Sorniotti & Francesco Bottiglione & Giacomo Mantriota & Pietro Perlo, 2020. "On the Comparison of 2- and 4-Wheel-Drive Electric Vehicle Layouts with Central Motors and Single- and 2-Speed Transmission Systems," Energies, MDPI, vol. 13(13), pages 1-24, June.
    14. Barbour, Natalia & Menon, Nikhil & Zhang, Yu & Mannering, Fred, 2019. "Shared automated vehicles: A statistical analysis of consumer use likelihoods and concerns," Transport Policy, Elsevier, vol. 80(C), pages 86-93.
    15. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    16. Devon McAslan & Farah Najar Arevalo & David A. King & Thaddeus R. Miller, 2021. "Pilot project purgatory? Assessing automated vehicle pilot projects in U.S. cities," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-16, December.
    17. Parlikar, Anupam & Truong, Cong Nam & Jossen, Andreas & Hesse, Holger, 2021. "The carbon footprint of island grids with lithium-ion battery systems: An analysis based on levelized emissions of energy supply," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    18. Yuan, Weichang & Frey, H. Christopher, 2020. "Potential for metro rail energy savings and emissions reduction via eco-driving," Applied Energy, Elsevier, vol. 268(C).
    19. Parlikar, Anupam & Schott, Maximilian & Godse, Ketaki & Kucevic, Daniel & Jossen, Andreas & Hesse, Holger, 2023. "High-power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation," Applied Energy, Elsevier, vol. 333(C).
    20. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2232-:d:774275. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.