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Efficiency and drag in the power-based model of fuel consumption

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  • Akcelik, Rahmi

Abstract

The efficiency and drag parameters in the instantaneous fuel consumption model are explained by comparing the model with the original power-based model developed at Sydney University, and relating the two models to a conceptual model. Various efficiency factors internal to the vehicle system can be modelled as contributing to the efficiency parameter, or explicitly as power components. The single efficiency parameter in the original power model includes engine drag and all other internal inefficiency components of the vehicle system. On the other hand, the two efficiency parameters in the Australian Road Research Board (ARRB) model have been derived in such a way that they do not include the engine/internal drag in the steady-state driving mode. A fuel consumption model that uses a drag force component measured by coast-down (in neutral) should employ a nonconstant efficiency factor (i.e. a factor dependent on speed and acceleration rates). Otherwise, a satisfactory level of accuracy cannot be achieved, particularly if the prediction of fuel consumption during different modes of driving is required. If all power terms are modelled explicitly, then a basic (constant) engine efficiency parameter can be employed. The basic efficiency factors found from engine maps are of the order of 0.06 to 0.08, which are very close to the values obtained for the ARRB model. This confirms the accuracy of the calibration method used for the ARRB model.

Suggested Citation

  • Akcelik, Rahmi, 1989. "Efficiency and drag in the power-based model of fuel consumption," Transportation Research Part B: Methodological, Elsevier, vol. 23(5), pages 376-385, October.
    • Handle: RePEc:eee:transb:v:23:y:1989:i:5:p:376-385
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    Cited by:

    1. Chen Wang & Yulu Dai & Jingxin Xia, 2020. "A CAV Platoon Control Method for Isolated Intersections: Guaranteed Feasible Multi-Objective Approach with Priority," Energies, MDPI, vol. 13(3), pages 1-16, February.
    2. Huifu Jiang & Shi An & Jian Wang & Jianxun Cui, 2018. "Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection," Sustainability, MDPI, vol. 10(1), pages 1-20, January.
    3. Gennaro Nicola Bifulco & Francesco Galante & Luigi Pariota & Maria Russo Spena, 2015. "A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems," Sustainability, MDPI, vol. 7(10), pages 1-18, October.
    4. Huifu Jiang & Wei Zhou & Chang Liu & Guosheng Zhang & Meng Hu, 2020. "Safe and Ecological Speed Control for Heavy-Duty Vehicles on Long–Steep Downhill and Sharp-Curved Roads," Sustainability, MDPI, vol. 12(17), pages 1-35, August.
    5. Wang, Tao & Yuan, Zijian & Zhang, Yuanshu & Zhang, Jing & Tian, Junfang, 2023. "A driving guidance strategy with pre-stop line at signalized intersection: Collaborative optimization of capacity and fuel consumption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    6. Huifu Jiang & Jia Hu & Byungkyu Brian Park & Meng Wang & Wei Zhou, 2019. "An Extensive Investigation of an Eco-Approach Controller under a Partially Connected and Automated Vehicle Environment," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    7. Yao, Zhihong & Wang, Yi & Liu, Bo & Zhao, Bin & Jiang, Yangsheng, 2021. "Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway," Energy, Elsevier, vol. 230(C).
    8. Yanyan Chen & Siyang Li & Yanan Li, 2023. "A Review on Quantitative Energy Consumption Models from Road Transportation," Energies, MDPI, vol. 17(1), pages 1-14, December.

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