Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine
Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a...
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MDPI AG
2021-12-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/10/12/330 |
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| author | Youding Sun Zhongpan Zhu Aimin Du Xinwen Chen |
| author_facet | Youding Sun Zhongpan Zhu Aimin Du Xinwen Chen |
| author_sort | Youding Sun |
| collection | DOAJ |
| description | Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future. |
| first_indexed | 2024-03-10T04:41:43Z |
| format | Article |
| id | doaj.art-9960f7256d6e47e4adc751905f1ed7e0 |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T04:41:43Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-9960f7256d6e47e4adc751905f1ed7e02023-11-23T03:16:43ZengMDPI AGActuators2076-08252021-12-01101233010.3390/act10120330Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive EngineYouding Sun0Zhongpan Zhu1Aimin Du2Xinwen Chen3College of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaMultiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future.https://www.mdpi.com/2076-0825/10/12/330cyberphysical systemclustering genetic algorithmmultiparameter optimizationautomotive engineenergy-saving |
| spellingShingle | Youding Sun Zhongpan Zhu Aimin Du Xinwen Chen Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine Actuators cyberphysical system clustering genetic algorithm multiparameter optimization automotive engine energy-saving |
| title | Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine |
| title_full | Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine |
| title_fullStr | Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine |
| title_full_unstemmed | Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine |
| title_short | Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine |
| title_sort | multiparameter optimization framework of cyberphysical systems a case study on energy saving of the automotive engine |
| topic | cyberphysical system clustering genetic algorithm multiparameter optimization automotive engine energy-saving |
| url | https://www.mdpi.com/2076-0825/10/12/330 |
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