NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network
NOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to streng...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/2/336 |
| _version_ | 1827372401282777088 |
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| author | Zhihong Wang Kai Feng |
| author_facet | Zhihong Wang Kai Feng |
| author_sort | Zhihong Wang |
| collection | DOAJ |
| description | NOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to strengthen the supervision of diesel vehicles for emission monitoring, this article intends to establish a model that can predict the transient emission characteristics of heavy-duty diesel vehicles and provide a solution for remote online monitoring of diesel vehicles. This paper refers to the heavy-duty vehicle National VI emission regulations and uses vehicle-mounted portable emission testing equipment (PEMS) to conduct actual road emission tests on a certain country’s VI heavy-duty diesel vehicles. Then, it proposes a new feature engineering processing method that uses gray correlation analysis and principal component analysis to eliminate invalid data and reduce the dimensionality of the aligned data, which facilitates the rapid convergence of the model during the training process. Then, a double-hidden-layer BP (Back propagation) neural network was established, and the improved gray wolf algorithm was used to optimize the threshold and weight of the neural network, and a heavy-duty diesel vehicle NOx emission prediction model was obtained. Through the training of the network, the root mean square error (RMSE) of the improved model on the test set between the predicted value and the true value is 1.9144 (mg/s), and the coefficient of determination (R<sup>2</sup>) is 0.87024. Compared with single-hidden-layer network and double-hidden-layer BP neural network models, the accuracy of the model has been improved. The model can well predict the actual road NOx emissions of heavy-duty diesel vehicles. |
| first_indexed | 2024-03-08T10:58:07Z |
| format | Article |
| id | doaj.art-5c2d0d7e48fc43a8b366f19302895d4f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T10:58:07Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5c2d0d7e48fc43a8b366f19302895d4f2024-01-26T16:16:41ZengMDPI AGEnergies1996-10732024-01-0117233610.3390/en17020336NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural NetworkZhihong Wang0Kai Feng1School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaNOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to strengthen the supervision of diesel vehicles for emission monitoring, this article intends to establish a model that can predict the transient emission characteristics of heavy-duty diesel vehicles and provide a solution for remote online monitoring of diesel vehicles. This paper refers to the heavy-duty vehicle National VI emission regulations and uses vehicle-mounted portable emission testing equipment (PEMS) to conduct actual road emission tests on a certain country’s VI heavy-duty diesel vehicles. Then, it proposes a new feature engineering processing method that uses gray correlation analysis and principal component analysis to eliminate invalid data and reduce the dimensionality of the aligned data, which facilitates the rapid convergence of the model during the training process. Then, a double-hidden-layer BP (Back propagation) neural network was established, and the improved gray wolf algorithm was used to optimize the threshold and weight of the neural network, and a heavy-duty diesel vehicle NOx emission prediction model was obtained. Through the training of the network, the root mean square error (RMSE) of the improved model on the test set between the predicted value and the true value is 1.9144 (mg/s), and the coefficient of determination (R<sup>2</sup>) is 0.87024. Compared with single-hidden-layer network and double-hidden-layer BP neural network models, the accuracy of the model has been improved. The model can well predict the actual road NOx emissions of heavy-duty diesel vehicles.https://www.mdpi.com/1996-1073/17/2/336PEMSheavy-duty diesel vehiclesNOx predictionprincipal component analysisimproved gray wolf algorithmBP neural network |
| spellingShingle | Zhihong Wang Kai Feng NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network Energies PEMS heavy-duty diesel vehicles NOx prediction principal component analysis improved gray wolf algorithm BP neural network |
| title | NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network |
| title_full | NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network |
| title_fullStr | NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network |
| title_full_unstemmed | NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network |
| title_short | NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network |
| title_sort | nox emission prediction for heavy duty diesel vehicles based on improved gwo bp neural network |
| topic | PEMS heavy-duty diesel vehicles NOx prediction principal component analysis improved gray wolf algorithm BP neural network |
| url | https://www.mdpi.com/1996-1073/17/2/336 |
| work_keys_str_mv | AT zhihongwang noxemissionpredictionforheavydutydieselvehiclesbasedonimprovedgwobpneuralnetwork AT kaifeng noxemissionpredictionforheavydutydieselvehiclesbasedonimprovedgwobpneuralnetwork |