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

Xinyi Zhu, Xiaoyan Shen, Kailiang Chen and Zeqing Zhang

Energy, 2024, vol. 296, issue C

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.

Keywords: Heavy duty trucks; Fuel consumption prediction; Machine learning; Model interpretation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009940

DOI: 10.1016/j.energy.2024.131221

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