A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors

Jian Gong, Junzhu Shang, Lei Li, Changjian Zhang, Jie He and Jinhang Ma
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Jian Gong: School of Transportation, Southeast University, Nanjing 210018, China
Junzhu Shang: School of Transportation, Southeast University, Nanjing 210018, China
Lei Li: School of Transportation, Southeast University, Nanjing 210018, China
Changjian Zhang: School of Transportation, Southeast University, Nanjing 210018, China
Jie He: School of Transportation, Southeast University, Nanjing 210018, China
Jinhang Ma: School of Transportation, Southeast University, Nanjing 210018, China

Energies, 2021, vol. 14, issue 23, 1-18

Abstract: With increasingly prominent environmental problems, controlling automobile exhaust has become essential to the environment. The fuel consumption of transportation is the critical factor that determines exhaust gas. By analyzing the naturalistic driving data of heavy-duty diesel trucks (HDDTs), this paper explored the influence of engine technical state, road features, weather, and temperature conditions on fuel consumption during driving. The detailed process is as follows: Firstly, we collected 1153 naturalistic driving data from 34 HDDTs and made a specific analysis and summary description of the data; secondly, by establishing a binary Logistic regression model, we quantitatively explored the influence of significant factors on the fuel consumption; meanwhile, based on quantitative analysis of factor’s effectiveness, this research used several machine learning algorithms (back-propagation neural network, decision tree, and random forest) to build fuel consumption predictors, and compared the prediction performance of different algorithms. The results showed that the prediction accuracy of the decision tree, back-propagation (BP) neural network, and random forest is 81.38%, 83.98%, and 86.58%, respectively. The random forest showed the best performance in predicting. The conclusions can assist transportation companies in formulating driving training strategies and contribute to reducing energy consumption and emissions.

Keywords: environmental protection; fleet management system; heavy-duty diesel trucks; prediction of fuel consumption; binary Logistic regression; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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