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Fuel consumption model optimization based on transient correction

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  • Guang, Hao
  • Jin, Hui

Abstract

With the growing desire for “lucid waters and lush mountains”, energy savings and emission reductions of automobiles have received unprecedented attention. As a result, how to achieve economical driving is also a focus of current research. The engine dynamic fuel consumption model is the basis for research on automobile fuel economy. To overcome the deficiency of the original transient fuel consumption models based on “steady-state prediction plus transient correction”, a new model called BIT-TFCM-3 was developed. The new model was verified using measured fuel consumption data from Argonne National Laboratory. The results show that, compared with the original models, the new model not only has a higher computing speed but also a significantly improved prediction accuracy.

Suggested Citation

  • Guang, Hao & Jin, Hui, 2019. "Fuel consumption model optimization based on transient correction," Energy, Elsevier, vol. 169(C), pages 508-514.
    • Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:508-514
    DOI: 10.1016/j.energy.2018.12.067
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    References listed on IDEAS

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    1. Fabio Chiara & Junmin Wang & Chinmaya B. Patil & Ming-Feng Hsieh & Fengjun Yan, 2011. "Development and experimental validation of a control-oriented Diesel engine model for fuel consumption and brake torque predictions," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 17(3), pages 261-277, January.
    2. Tang, Tie-Qiao & Wang, Tao & Chen, Liang & Shang, Hua-Yan, 2017. "Analysis of the trip costs of a traffic corridor with two entrances and one exit under car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 720-729.
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    Citations

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    Cited by:

    1. Jin, Yue & Yang, Lin & Du, Mao & Qiang, Jiaxi & Li, Jingzhong & Chen, Yuxuan & Tu, Jiayu, 2023. "Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction," Energy, Elsevier, vol. 267(C).
    2. Baodi Zhang & Sheng Guo & Xin Zhang & Qicheng Xue & Lan Teng, 2020. "Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 13(8), pages 1-25, April.
    3. Hugo Ferreira & Carlos Manuel Rodrigues & Carlos Pinho, 2019. "Impact of Road Geometry on Vehicle Energy Consumption and CO 2 Emissions: An Energy-Efficiency Rating Methodology," Energies, MDPI, vol. 13(1), pages 1-27, December.
    4. Piotr Bera, 2019. "Development of Engine Efficiency Characteristic in Dynamic Working States," Energies, MDPI, vol. 12(15), pages 1-14, July.
    5. Kroyan, Yuri & Wojcieszyk, Michal & Kaario, Ossi & Larmi, Martti & Zenger, Kai, 2020. "Modeling the end-use performance of alternative fuels in light-duty vehicles," Energy, Elsevier, vol. 205(C).

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