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Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM

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  • Zhu, Xinyi
  • Shen, Xiaoyan
  • Chen, Kailiang
  • Zhang, Zeqing

Abstract

Excessive fuel consumption of heavy duty trucks aggravates environmental pollution and leads to reduced economic benefits of road transportation enterprises. In order to study the influencing factors of fuel consumption of heavy duty trucks, based on real-time vehicle operating parameter data, this paper establishes a segment fuel consumption prediction model based on LightGBM algorithm. The fitting ability of the model is improved by feature selection and Optuna hyperparameter search. The performance is compared with other models. The results show that the prediction effect of LightGBM model is optimal. Finally, based on the SHAP framework, the prediction results of the optimal fragmented fuel consumption model are visually explained. The conclusion that average speed and average throttle pedal value have a significant impact on segmented fuel consumption. Ensuring higher and more stable speeds, reducing the frequency of acceleration and deceleration can reduce the fuel consumption appropriately.

Suggested Citation

  • Zhu, Xinyi & Shen, Xiaoyan & Chen, Kailiang & Zhang, Zeqing, 2024. "Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM," Energy, Elsevier, vol. 296(C).
  • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009940
    DOI: 10.1016/j.energy.2024.131221
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    References listed on IDEAS

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    1. Jakov Topić & Branimir Škugor & Joško Deur, 2022. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data," Sustainability, MDPI, vol. 14(2), pages 1-12, January.
    2. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    3. Ning Yang & Lei Yang & Feng Xu & Xue Han & Bin Liu & Naiyuan Zheng & Yuan Li & Yu Bai & Liwei Li & Jiguang Wang, 2022. "Vehicle Emission Changes in China under Different Control Measures over Past Two Decades," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    4. Mulholland, Eamonn & Teter, Jacob & Cazzola, Pierpaolo & McDonald, Zane & Ó Gallachóir, Brian P., 2018. "The long haul towards decarbonising road freight – A global assessment to 2050," Applied Energy, Elsevier, vol. 216(C), pages 678-693.
    5. 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.
    6. Jian Gong & Junzhu Shang & Lei Li & Changjian Zhang & Jie He & Jinhang Ma, 2021. "A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors," Energies, MDPI, vol. 14(23), pages 1-18, December.
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