Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion mode
Recent market trends suggest that internal combustion engines will still have a significant role in transportation. Commercial diesel engines have to be further developed to meet strict environmental legislation. Low temperature combustion (LTC) is a new technology that can enhance the efficiency an...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | International Journal of Thermofluids |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666202725002216 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849700138113564672 |
|---|---|
| author | Márton Virt Máté Zöldy |
| author_facet | Márton Virt Máté Zöldy |
| author_sort | Márton Virt |
| collection | DOAJ |
| description | Recent market trends suggest that internal combustion engines will still have a significant role in transportation. Commercial diesel engines have to be further developed to meet strict environmental legislation. Low temperature combustion (LTC) is a new technology that can enhance the efficiency and reduce the pollutant emissions of compression ignition engines. However, some LTC methods deal with problematic combustion control that could be handled by model predictive cycle-to-cycle control. The applied physicochemical models require high computational capacity, increasing engine control costs. Artificial intelligence (AI) can provide a better alternative approach since well-trained AI models can perform calculations much faster. Therefore, this work aims to create AI models to predict combustion characteristics under varying conditions, including conventional and LTC operations. 113 different intake oxygen concentrations were set by exhaust gas recirculation, and the heat release rates and combustion pressures were predicted with neural networks. The results demonstrated that the AI can accurately predict both characteristics, providing an excellent possibility for further research where an AI-based model predictive controller could be built. |
| format | Article |
| id | doaj-art-82742cf8f487424fa642201e0055d06b |
| institution | DOAJ |
| issn | 2666-2027 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Thermofluids |
| spelling | doaj-art-82742cf8f487424fa642201e0055d06b2025-08-20T03:18:23ZengElsevierInternational Journal of Thermofluids2666-20272025-05-012710127410.1016/j.ijft.2025.101274Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion modeMárton Virt0Máté Zöldy1Corresponding author.; Budapest University of Technology and Economics, Department of Automotive Technologies, Műegyetem rkp. 3., 1111 Budapest, HungaryBudapest University of Technology and Economics, Department of Automotive Technologies, Műegyetem rkp. 3., 1111 Budapest, HungaryRecent market trends suggest that internal combustion engines will still have a significant role in transportation. Commercial diesel engines have to be further developed to meet strict environmental legislation. Low temperature combustion (LTC) is a new technology that can enhance the efficiency and reduce the pollutant emissions of compression ignition engines. However, some LTC methods deal with problematic combustion control that could be handled by model predictive cycle-to-cycle control. The applied physicochemical models require high computational capacity, increasing engine control costs. Artificial intelligence (AI) can provide a better alternative approach since well-trained AI models can perform calculations much faster. Therefore, this work aims to create AI models to predict combustion characteristics under varying conditions, including conventional and LTC operations. 113 different intake oxygen concentrations were set by exhaust gas recirculation, and the heat release rates and combustion pressures were predicted with neural networks. The results demonstrated that the AI can accurately predict both characteristics, providing an excellent possibility for further research where an AI-based model predictive controller could be built.http://www.sciencedirect.com/science/article/pii/S2666202725002216Artificial intelligenceLow temperature combustionCombustion modelling |
| spellingShingle | Márton Virt Máté Zöldy Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion mode International Journal of Thermofluids Artificial intelligence Low temperature combustion Combustion modelling |
| title | Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion mode |
| title_full | Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion mode |
| title_fullStr | Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion mode |
| title_full_unstemmed | Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion mode |
| title_short | Towards cycle-to-cycle control: AI-based heat release rate prediction of a diesel engine operated in low temperature combustion mode |
| title_sort | towards cycle to cycle control ai based heat release rate prediction of a diesel engine operated in low temperature combustion mode |
| topic | Artificial intelligence Low temperature combustion Combustion modelling |
| url | http://www.sciencedirect.com/science/article/pii/S2666202725002216 |
| work_keys_str_mv | AT martonvirt towardscycletocyclecontrolaibasedheatreleaseratepredictionofadieselengineoperatedinlowtemperaturecombustionmode AT matezoldy towardscycletocyclecontrolaibasedheatreleaseratepredictionofadieselengineoperatedinlowtemperaturecombustionmode |