Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired
It has been recognized that emission control for heavy diesel trucks should be given priority, as a massive amount of pollutants (e.g., NO<sub>x</sub>) are emitted from heavy diesel trucks. Although pollutants can be filtered to a considerable extent by after-treatment devices equipment,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/6/924 |
| _version_ | 1827662459125628928 |
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| author | Weinan He Xiaobin Zheng Yumeng Zhang Yuan Han |
| author_facet | Weinan He Xiaobin Zheng Yumeng Zhang Yuan Han |
| author_sort | Weinan He |
| collection | DOAJ |
| description | It has been recognized that emission control for heavy diesel trucks should be given priority, as a massive amount of pollutants (e.g., NO<sub>x</sub>) are emitted from heavy diesel trucks. Although pollutants can be filtered to a considerable extent by after-treatment devices equipment, emissions can still exceed the designated standards when after-treatment devices function improperly. To timely identify excessive emissions, we propose a general and systematic framework, including a data quality assessment and a data repairing and excessive emission determination process, based on the data sensed from the on-board diagnostics (OBD) monitoring system. To overcome the adverse effects of poor data quality, a set of approaches have been developed for the different statuses of data quality. When all variables contain missing or abnormal values, data repairing algorithms can be employed to improve data quality. Two strategies have been developed for the situation where only the NO<sub>x</sub> data is problematic. One is to improve data quality by using the other variables before identifying excessive emissions, and the other is to directly predict whether the emissions exceed recommendations by using other variables without the data quality problem. To reduce the impact of noise and extreme values, three methods based on the moving average principle have been developed to generate an aggregated emission level for the determination of excessive emissions. In the experimental study, we employed a number of machine learning algorithms to achieve data repairing and prediction. The support vector machine (SVM) algorithm slightly outperforms the random forests (RF) and gradient boosting decision tree (GBDT) in the prediction of the excessive emission possibility in terms of prediction accuracy. The experimental results indicate that the most accurate data repairing can be achieved by probabilistic principal component analysis (PPCA), as compared to non-negative matrix factorization (NNMF) and k-nearest neighbor (KNN). However, the proposed approach does not restrict other algorithms from achieving the functions of data repairing and prediction. |
| first_indexed | 2024-03-10T00:26:19Z |
| format | Article |
| id | doaj.art-133f081640db4c36abcc446d2f11e607 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T00:26:19Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-133f081640db4c36abcc446d2f11e6072023-11-23T15:32:52ZengMDPI AGAtmosphere2073-44332022-06-0113692410.3390/atmos13060924Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data RepairedWeinan He0Xiaobin Zheng1Yumeng Zhang2Yuan Han3Beijing Transport Institute, Beijing Key Laboratory of Transport Energy Conservation and Emission Reduction, Beijing 100073, ChinaBeijing Transport Institute, Beijing Key Laboratory of Transport Energy Conservation and Emission Reduction, Beijing 100073, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Transport Institute, Beijing Key Laboratory of Transport Energy Conservation and Emission Reduction, Beijing 100073, ChinaIt has been recognized that emission control for heavy diesel trucks should be given priority, as a massive amount of pollutants (e.g., NO<sub>x</sub>) are emitted from heavy diesel trucks. Although pollutants can be filtered to a considerable extent by after-treatment devices equipment, emissions can still exceed the designated standards when after-treatment devices function improperly. To timely identify excessive emissions, we propose a general and systematic framework, including a data quality assessment and a data repairing and excessive emission determination process, based on the data sensed from the on-board diagnostics (OBD) monitoring system. To overcome the adverse effects of poor data quality, a set of approaches have been developed for the different statuses of data quality. When all variables contain missing or abnormal values, data repairing algorithms can be employed to improve data quality. Two strategies have been developed for the situation where only the NO<sub>x</sub> data is problematic. One is to improve data quality by using the other variables before identifying excessive emissions, and the other is to directly predict whether the emissions exceed recommendations by using other variables without the data quality problem. To reduce the impact of noise and extreme values, three methods based on the moving average principle have been developed to generate an aggregated emission level for the determination of excessive emissions. In the experimental study, we employed a number of machine learning algorithms to achieve data repairing and prediction. The support vector machine (SVM) algorithm slightly outperforms the random forests (RF) and gradient boosting decision tree (GBDT) in the prediction of the excessive emission possibility in terms of prediction accuracy. The experimental results indicate that the most accurate data repairing can be achieved by probabilistic principal component analysis (PPCA), as compared to non-negative matrix factorization (NNMF) and k-nearest neighbor (KNN). However, the proposed approach does not restrict other algorithms from achieving the functions of data repairing and prediction.https://www.mdpi.com/2073-4433/13/6/924heavy diesel trucksexcessive emission determinationOBD datadata repairing |
| spellingShingle | Weinan He Xiaobin Zheng Yumeng Zhang Yuan Han Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired Atmosphere heavy diesel trucks excessive emission determination OBD data data repairing |
| title | Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired |
| title_full | Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired |
| title_fullStr | Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired |
| title_full_unstemmed | Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired |
| title_short | Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired |
| title_sort | study on determination of excessive emissions of heavy diesel trucks based on obd data repaired |
| topic | heavy diesel trucks excessive emission determination OBD data data repairing |
| url | https://www.mdpi.com/2073-4433/13/6/924 |
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