Learning Based Model Predictive Control of Combustion Timing in Multi-Cylinder Partially Premixed Combustion Engine
(2019) In SAE Technical Papers- Abstract
- Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC... (More)
- Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC controller utilizes the main injection timing to manage the combustion timing. The cylinder pressure is used as the combustion feedback. The method is validated in a multi-cylinder heavy-duty PPC engine for transient control. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3c67f93d-44b6-46f8-8747-7df6fe3783a8
- author
- Li, Xiufei LU ; Yin, Lianhao LU ; Tunestål, Per LU and Johansson, Rolf LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- in
- SAE Technical Papers
- article number
- Technical Paper 2019-24-0016
- publisher
- Society of Automotive Engineers
- external identifiers
-
- scopus:85085864235
- ISSN
- 0148-7191
- DOI
- 10.4271/2019-24-0016
- project
- Competence Centre for Combustion Processes
- language
- English
- LU publication?
- yes
- id
- 3c67f93d-44b6-46f8-8747-7df6fe3783a8
- date added to LUP
- 2020-03-12 16:36:16
- date last changed
- 2022-04-18 21:29:30
@article{3c67f93d-44b6-46f8-8747-7df6fe3783a8, abstract = {{Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC controller utilizes the main injection timing to manage the combustion timing. The cylinder pressure is used as the combustion feedback. The method is validated in a multi-cylinder heavy-duty PPC engine for transient control.}}, author = {{Li, Xiufei and Yin, Lianhao and Tunestål, Per and Johansson, Rolf}}, issn = {{0148-7191}}, language = {{eng}}, publisher = {{Society of Automotive Engineers}}, series = {{SAE Technical Papers}}, title = {{Learning Based Model Predictive Control of Combustion Timing in Multi-Cylinder Partially Premixed Combustion Engine}}, url = {{http://dx.doi.org/10.4271/2019-24-0016}}, doi = {{10.4271/2019-24-0016}}, year = {{2019}}, }