CatBoost-based fuel consumption modeling and explainable analysis for heavy-duty diesel trucks: Impact of engine, driving behavior, and vehicle weight

Anran Liu, Pengfei Fan, Xiaoyu He, Hongyu Lu, Lei Yu and Guohua Song

Energy, 2025, vol. 333, issue C

Abstract: The limited understanding of complex causal relationships and interactions among influencing factors and fuel consumption hinders the development of high-precision fuel consumption models and restricts their practical application. This study leverages high-resolution operational data from 7311 in-use heavy-duty diesel trucks to model the impacts of engine parameters, driving behavior, and vehicle weight on fuel consumption using machine learning. Specifically, six feature availability scenarios were designed, and CatBoost-based prediction models were developed to evaluate model robustness under missing feature conditions. SHapley Additive exPlanations (SHAP) were further applied for post-hoc analysis to reveal the global, individual, and interaction-level effects of key features on fuel consumption. Results show that models incorporating engine features yielded the highest accuracy, achieving a minimum MAPE of 5.62 %, and exhibited strong robustness to the exclusion of other feature categories. In contrast, without engine features, the best MAPE rose to 11.60 %, and vehicle weight became the most critical predictor; omitting it led to a further 7.96 % rise in prediction error. Vehicle load rate has a clear positive effect on fuel consumption, and the effect of driving behavior varies with loading conditions. Under high-speed conditions (>80 km/h), maintaining engine speed within the economical range effectively reduces fuel consumption. Additionally, high engine load significantly increases the sensitivity of fuel consumption to friction torque.

Keywords: Fuel consumption; Vehicle weight; Heavy-duty diesel trucks; Machine learning; Explainable artificial intelligence (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225031275
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225031275

DOI: 10.1016/j.energy.2025.137485

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().