A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/23/8106 |
| _version_ | 1797507858255839232 |
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| author | Jian Gong Junzhu Shang Lei Li Changjian Zhang Jie He Jinhang Ma |
| author_facet | Jian Gong Junzhu Shang Lei Li Changjian Zhang Jie He Jinhang Ma |
| author_sort | Jian Gong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:54:23Z |
| format | Article |
| id | doaj.art-83ea908b76c344c6af7e8c2eff3ce0ea |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:54:23Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-83ea908b76c344c6af7e8c2eff3ce0ea2023-11-23T02:22:40ZengMDPI AGEnergies1996-10732021-12-011423810610.3390/en14238106A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing FactorsJian Gong0Junzhu Shang1Lei Li2Changjian Zhang3Jie He4Jinhang Ma5School of Transportation, Southeast University, Nanjing 210018, ChinaSchool of Transportation, Southeast University, Nanjing 210018, ChinaSchool of Transportation, Southeast University, Nanjing 210018, ChinaSchool of Transportation, Southeast University, Nanjing 210018, ChinaSchool of Transportation, Southeast University, Nanjing 210018, ChinaSchool of Transportation, Southeast University, Nanjing 210018, ChinaWith 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.https://www.mdpi.com/1996-1073/14/23/8106environmental protectionfleet management systemheavy-duty diesel trucksprediction of fuel consumptionbinary Logistic regressionmachine learning |
| spellingShingle | Jian Gong Junzhu Shang Lei Li Changjian Zhang Jie He Jinhang Ma A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors Energies environmental protection fleet management system heavy-duty diesel trucks prediction of fuel consumption binary Logistic regression machine learning |
| title | A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors |
| title_full | A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors |
| title_fullStr | A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors |
| title_full_unstemmed | A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors |
| title_short | A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors |
| title_sort | comparative study on fuel consumption prediction methods of heavy duty diesel trucks considering 21 influencing factors |
| topic | environmental protection fleet management system heavy-duty diesel trucks prediction of fuel consumption binary Logistic regression machine learning |
| url | https://www.mdpi.com/1996-1073/14/23/8106 |
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