Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm
In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/3/1756 |
| _version_ | 1797488908009734144 |
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| author | Ying Sun Lin Lv Peng Lee Yunkai Cai |
| author_facet | Ying Sun Lin Lv Peng Lee Yunkai Cai |
| author_sort | Ying Sun |
| collection | DOAJ |
| description | In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test results, and the time of an engine test is so long that the test results cannot meet the real-time requirement. Therefore, it is necessary to develop technology with high accuracy and a fast response to predict the in-cylinder pressure of diesel engines. In this paper, the in-cylinder pressure values of a high-speed diesel engine under different conditions are used to train the extreme gradient boosting model, and the sparrow search algorithm—which belongs to the swarm intelligence optimization algorithm—is introduced to optimize the hyper parameters of the model. The research results show that the extreme gradient boosting model combined with the sparrow search algorithm can predict the in-cylinder pressure under each verification condition with high accuracy, and the proportion of the samples which prediction error is less than 10% in the validation set is 94%. In the process of model optimization, it is found that compared with the grid search method, the sparrow search algorithm has stronger hyper parameter optimization ability, which reduces the mean square error of the prediction model by 27.99%. |
| first_indexed | 2024-03-10T00:09:01Z |
| format | Article |
| id | doaj.art-4a36956669b149ea8bce10318274563b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:09:01Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4a36956669b149ea8bce10318274563b2023-11-23T16:02:57ZengMDPI AGApplied Sciences2076-34172022-02-01123175610.3390/app12031756Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search AlgorithmYing Sun0Lin Lv1Peng Lee2Yunkai Cai3School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaIn-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test results, and the time of an engine test is so long that the test results cannot meet the real-time requirement. Therefore, it is necessary to develop technology with high accuracy and a fast response to predict the in-cylinder pressure of diesel engines. In this paper, the in-cylinder pressure values of a high-speed diesel engine under different conditions are used to train the extreme gradient boosting model, and the sparrow search algorithm—which belongs to the swarm intelligence optimization algorithm—is introduced to optimize the hyper parameters of the model. The research results show that the extreme gradient boosting model combined with the sparrow search algorithm can predict the in-cylinder pressure under each verification condition with high accuracy, and the proportion of the samples which prediction error is less than 10% in the validation set is 94%. In the process of model optimization, it is found that compared with the grid search method, the sparrow search algorithm has stronger hyper parameter optimization ability, which reduces the mean square error of the prediction model by 27.99%.https://www.mdpi.com/2076-3417/12/3/1756diesel enginein-cylinder pressurepredictionmachine learningswarm intelligence optimization algorithm |
| spellingShingle | Ying Sun Lin Lv Peng Lee Yunkai Cai Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm Applied Sciences diesel engine in-cylinder pressure prediction machine learning swarm intelligence optimization algorithm |
| title | Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm |
| title_full | Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm |
| title_fullStr | Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm |
| title_full_unstemmed | Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm |
| title_short | Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm |
| title_sort | prediction of in cylinder pressure of diesel engine based on extreme gradient boosting and sparrow search algorithm |
| topic | diesel engine in-cylinder pressure prediction machine learning swarm intelligence optimization algorithm |
| url | https://www.mdpi.com/2076-3417/12/3/1756 |
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