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Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning

Author

Listed:
  • Kibok Kim

    (Department of Mechanical Engineering, Ajou University, 206 World Cup-ro, Yeongtong-gu, Suwon 16499, Korea
    Vehicle Calibration Team, Tenergy, 145 Gwanggyo-ro, Yeongtong-gu, Suwon 16229, Korea)

  • Jinil Park

    (Department of Mechanical Engineering, Ajou University, 206 World Cup-ro, Yeongtong-gu, Suwon 16499, Korea)

  • Jonghwa Lee

    (Department of Mechanical Engineering, Ajou University, 206 World Cup-ro, Yeongtong-gu, Suwon 16499, Korea)

Abstract

Eco-drive is a widely used concept. It can improve fuel economy for different driving behaviors such as vehicle acceleration or accelerator pedal operation, deceleration or coasting while slowing down, and gear shift timing difference. The feasibility of improving the fuel economy of urban buses by applying eco-drive was verified by analyzing data from drivers who achieved high fuel efficiencies in urban buses with a high frequency of acceleration/deceleration and frequent operation. The items that were monitored for eco-drive were: rapid take-off/acceleration/deceleration, accelerator pedal gradient, coasting rate, shift indicator violation, average engine speed, over speed, and gear shifting under low-end engine speed. The monitoring method for each monitored item was set up, and an index was produced using driving data. A fuel economy prediction model was created using machine learning to determine the contribution of each index to the fuel economy. Furthermore, the contribution of each monitoring item was analyzed using the prediction model explainer. Accordingly, points (defined as the eco-drive score) were allocated for each monitoring item. It was verified that this score can represent the eco-drive characteristics based on the relationship between the score and fuel economy. In addition, it resulted in an average annual fuel economy improvement of 12.1%.

Suggested Citation

  • Kibok Kim & Jinil Park & Jonghwa Lee, 2021. "Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning," Energies, MDPI, vol. 14(15), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4471-:d:600509
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    References listed on IDEAS

    as
    1. Emilia M. Szumska & Rafał Jurecki, 2020. "The Effect of Aggressive Driving on Vehicle Parameters," Energies, MDPI, vol. 13(24), pages 1-15, December.
    2. 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.
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    Cited by:

    1. Hongli Liu & Weiguo Yun & Bin Li & Mengling Dai & Yangyuhang Wang, 2023. "A Quantitative Study on Driving Behavior Economy Based on Big Data from the Pure Electric Bus," Sustainability, MDPI, vol. 15(10), pages 1-16, May.

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