IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i7p2031-d531154.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/7/2031/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/7/2031/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. 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.

    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. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    2. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    3. Yurii Gutarevych & Vasyl Mateichyk & Jonas Matijošius & Alfredas Rimkus & Igor Gritsuk & Oleksander Syrota & Yevheniy Shuba, 2020. "Improving Fuel Economy of Spark Ignition Engines Applying the Combined Method of Power Regulation," Energies, MDPI, vol. 13(5), pages 1-19, March.
    4. Wang, Xu & Sun, Huijun & Zhang, Si & Lv, Ying & Li, Tongfei, 2022. "Bike sharing rebalancing problem with variable demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    5. Yu, Yang & Wang, Sihan & Wang, Junwei & Huang, Min, 2019. "A branch-and-price algorithm for the heterogeneous fleet green vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 511-527.
    6. Duwon Choi & Youngkuk An & Nankyu Lee & Jinil Park & Jonghwa Lee, 2020. "Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System," Energies, MDPI, vol. 13(20), pages 1-24, October.
    7. Shang, Wen-Long & Zhang, Mengxiao & Wu, Guoyuan & Yang, Lan & Fang, Shan & Ochieng, Washington, 2023. "Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles," Applied Energy, Elsevier, vol. 351(C).
    8. Zhuowu Zhang & Emrah Demir & Robert Mason & Carla Cairano-Gilfedder, 2023. "Understanding freight drivers' behavior and the impact on vehicles' fuel consumption and CO2e emissions," Operational Research, Springer, vol. 23(4), pages 1-35, December.
    9. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    10. Behnke, Martin & Kirschstein, Thomas & Bierwirth, Christian, 2021. "A column generation approach for an emission-oriented vehicle routing problem on a multigraph," European Journal of Operational Research, Elsevier, vol. 288(3), pages 794-809.
    11. Emna Marrekchi & Walid Besbes & Diala Dhouib & Emrah Demir, 2021. "A review of recent advances in the operations research literature on the green routing problem and its variants," Annals of Operations Research, Springer, vol. 304(1), pages 529-574, September.
    12. Rosario Carbone & Concettina Marino & Antonino Nucara & Maria Francesca Panzera & Matilde Pietrafesa, 2019. "Electric Load Influence on Performances of a Composite Plant for Hydrogen Production from RES and its Conversion in Electricity," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    13. Ma, Jing & Sun, Yongfei & Zhang, Shiang, 2023. "Experimental investigation on energy consumption of power battery integrated thermal management system," Energy, Elsevier, vol. 270(C).
    14. Zhipeng Ma & Bo Nørregaard Jørgensen & Zheng Ma, 2024. "A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods," Energies, MDPI, vol. 17(2), pages 1-19, January.
    15. Qiu, Rui & Xu, Jiuping & Ke, Ruimin & Zeng, Ziqiang & Wang, Yinhai, 2020. "Carbon pricing initiatives-based bi-level pollution routing problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 203-217.
    16. Raeesi, Ramin & Zografos, Konstantinos G., 2019. "The multi-objective Steiner pollution-routing problem on congested urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 457-485.
    17. Yan Wang & Qing Gao & Tianshi Zhang & Guohua Wang & Zhipeng Jiang & Yunxia Li, 2017. "Advances in Integrated Vehicle Thermal Management and Numerical Simulation," Energies, MDPI, vol. 10(10), pages 1-30, October.
    18. Francesco Calise & Mário Costa & Qiuwang Wang & Xiliang Zhang & Neven Duić, 2018. "Recent Advances in the Analysis of Sustainable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-30, September.
    19. Chiang, Wen-Chyuan & Li, Yuyu & Shang, Jennifer & Urban, Timothy L., 2019. "Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization," Applied Energy, Elsevier, vol. 242(C), pages 1164-1175.
    20. Francesco Calise & Maria Vicidomini & Mário Costa & Qiuwang Wang & Poul Alberg Østergaard & Neven Duić, 2019. "Toward an Efficient and Sustainable Use of Energy in Industries and Cities," Energies, MDPI, vol. 12(16), pages 1-28, August.

    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:14:y:2021:i:7:p:2031-:d:531154. 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.