Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling
Control of combustion stability in partial pre-mixed compression ignition (PPCI) engine is one of the main issues facing its application. However, the multi-parameter coupling and nonlinear increase in the combustion process make the model and controller design more difficult. Therefore, this study...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000047 |
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| author | Ziqiang Chen Peng Ju Zhe Wang Du Huang Lei Shi Kangyao Deng |
| author_facet | Ziqiang Chen Peng Ju Zhe Wang Du Huang Lei Shi Kangyao Deng |
| author_sort | Ziqiang Chen |
| collection | DOAJ |
| description | Control of combustion stability in partial pre-mixed compression ignition (PPCI) engine is one of the main issues facing its application. However, the multi-parameter coupling and nonlinear increase in the combustion process make the model and controller design more difficult. Therefore, this study proposed a diesel engine control method that combines neural networks and model-free adaptive control in the absence of model and controller structure, which can achieve real-time coordination control of crank angle at 50 % of total heat release (CA50) and indicated mean effective pressure (IMEP) in the PPCI combustion process. Through comparisons under different operating conditions, it was found that the adjustment of algorithm parameters needs to adapt to the sensitivity changes of control parameters. In addition, the study validated the real-time performance and control effect of the algorithm, the experimental results indicate that the execution time of the control algorithm is approximately 5.59 milliseconds, which satisfies the real-time control requirements for the combustion process. By adjusting the weight coefficient matrix of the control authority, CA50 and IMEP are effectively tracked within the constraints of maximum pressure rise rate. The control error for CA50 remains within ±2.7 %, while that for IMEP is confined to ±1 %. Furthermore, the root mean square error for CA50 is measured at 1.1 crank angle, and for IMEP it stands at 23.5 kPa, thereby achieving precise real-time control of the PPCI combustion process. |
| format | Article |
| id | doaj-art-1049f81614a34df6bba6ba89c15d9cbd |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-1049f81614a34df6bba6ba89c15d9cbd2025-01-27T04:22:23ZengElsevierEnergy and AI2666-54682025-01-0119100472Research on multi-objective control of PPCI diesel engine combustion process based on data driven modellingZiqiang Chen0Peng Ju1Zhe Wang2Du Huang3Lei Shi4Kangyao Deng5Key Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaKey Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaKey Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaKey Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; Chongqing Hongjiang Machinery Co., Ltd., Chongqing 402162, ChinaKey Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; Corresponding author.Key Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaControl of combustion stability in partial pre-mixed compression ignition (PPCI) engine is one of the main issues facing its application. However, the multi-parameter coupling and nonlinear increase in the combustion process make the model and controller design more difficult. Therefore, this study proposed a diesel engine control method that combines neural networks and model-free adaptive control in the absence of model and controller structure, which can achieve real-time coordination control of crank angle at 50 % of total heat release (CA50) and indicated mean effective pressure (IMEP) in the PPCI combustion process. Through comparisons under different operating conditions, it was found that the adjustment of algorithm parameters needs to adapt to the sensitivity changes of control parameters. In addition, the study validated the real-time performance and control effect of the algorithm, the experimental results indicate that the execution time of the control algorithm is approximately 5.59 milliseconds, which satisfies the real-time control requirements for the combustion process. By adjusting the weight coefficient matrix of the control authority, CA50 and IMEP are effectively tracked within the constraints of maximum pressure rise rate. The control error for CA50 remains within ±2.7 %, while that for IMEP is confined to ±1 %. Furthermore, the root mean square error for CA50 is measured at 1.1 crank angle, and for IMEP it stands at 23.5 kPa, thereby achieving precise real-time control of the PPCI combustion process.http://www.sciencedirect.com/science/article/pii/S2666546825000047PPCIMFACNeural networkMulti-objective collaborationReal-time combustion control |
| spellingShingle | Ziqiang Chen Peng Ju Zhe Wang Du Huang Lei Shi Kangyao Deng Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling Energy and AI PPCI MFAC Neural network Multi-objective collaboration Real-time combustion control |
| title | Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling |
| title_full | Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling |
| title_fullStr | Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling |
| title_full_unstemmed | Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling |
| title_short | Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling |
| title_sort | research on multi objective control of ppci diesel engine combustion process based on data driven modelling |
| topic | PPCI MFAC Neural network Multi-objective collaboration Real-time combustion control |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000047 |
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