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Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

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Listed:
  • Isabella Yunfei Zeng

    (UK-China (Guangdong) CCUS Centre, Guangzhou 510663, China)

  • Shiqi Tan

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Jianliang Xiong

    (School of Economics and Management, Tsinghua University, Beijing 100084, China)

  • Xuesong Ding

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Yawen Li

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Tian Wu

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R 2 ) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

Suggested Citation

  • Isabella Yunfei Zeng & Shiqi Tan & Jianliang Xiong & Xuesong Ding & Yawen Li & Tian Wu, 2021. "Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners," Energies, MDPI, vol. 14(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7915-:d:687690
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    Cited by:

    1. Yushan Yang & Nuoya Gong & Keying Xie & Qingfei Liu, 2022. "Predicting Gasoline Vehicle Fuel Consumption in Energy and Environmental Impact Based on Machine Learning and Multidimensional Big Data," Energies, MDPI, vol. 15(5), pages 1-17, February.
    2. Dengfeng Zhao & Haiyang Li & Junjian Hou & Pengliang Gong & Yudong Zhong & Wenbin He & Zhijun Fu, 2023. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption," Energies, MDPI, vol. 16(14), pages 1-20, July.
    3. Seongin Jo & Hyung Jun Kim & Sang Il Kwon & Jong Tae Lee & Suhan Park, 2023. "Assessment of Energy Consumption Characteristics of Ultra-Heavy-Duty Vehicles under Real Driving Conditions," Energies, MDPI, vol. 16(5), pages 1-18, February.

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