Dynamic Mapping of Diesel Engine through System Identification
(2010) American Control Conference, 2010 In American Control Conference. Proceedings p.3015-3020- Abstract
- From a control design point of view, modern diesel engines are dynamic, nonlinear, MIMO systems. This paper presents a method to find low-complexity black-box dynamic models suitable for model predictive control (MPC) of NOx and soot emissions based on on-line emissions measurements. A four-input-five-output representation of the engine is considered, with fuel injection timing, fuel injection duration, exhaust gas recirculation (EGR) and variable geometry turbo (VGT) valve positions as inputs, and indicated mean effective pressure, combustion phasing, peak pressure derivative, NOx emissions, and soot emissions as outputs. Experimental data were collected on a six-cylinder heavy-duty engine at 30 operating points. The identification... (More)
- From a control design point of view, modern diesel engines are dynamic, nonlinear, MIMO systems. This paper presents a method to find low-complexity black-box dynamic models suitable for model predictive control (MPC) of NOx and soot emissions based on on-line emissions measurements. A four-input-five-output representation of the engine is considered, with fuel injection timing, fuel injection duration, exhaust gas recirculation (EGR) and variable geometry turbo (VGT) valve positions as inputs, and indicated mean effective pressure, combustion phasing, peak pressure derivative, NOx emissions, and soot emissions as outputs. Experimental data were collected on a six-cylinder heavy-duty engine at 30 operating points. The identification procedure starts by identifying local linear models at each operating point. To reduce the number of dynamic models necessary to describe the engine dynamics, Wiener
models are introduced and a clustering algorithm is proposed. A resulting set of two to five dynamic models is shown to be able to predict all outputs at all operating points with good accuracy. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1759171
- author
- Henningsson, Maria LU ; Ekholm, Kent LU ; Strandh, Petter LU ; Tunestål, Per LU and Johansson, Rolf LU
- organization
- publishing date
- 2010
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Diesel engines, System identification, Wiener models
- host publication
- Proceedings of the 2010 American Control Conference
- series title
- American Control Conference. Proceedings
- pages
- 3015 - 3020
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- American Control Conference, 2010
- conference location
- Baltimore, MD, United States
- conference dates
- 2010-06-30 - 2010-07-02
- external identifiers
-
- wos:000287187903066
- scopus:77957757802
- ISSN
- 2378-5861
- 0743-1619
- ISBN
- 978-1-4244-7427-1
- 978-1-4244-7426-4
- DOI
- 10.1109/ACC.2010.5531242
- project
- Diesel HCCI in a Multi-Cylinder Engine
- language
- English
- LU publication?
- yes
- id
- 44d7617e-b1e5-481f-bdc3-29928a357c95 (old id 1759171)
- date added to LUP
- 2016-04-04 14:00:23
- date last changed
- 2024-01-15 16:50:15
@inproceedings{44d7617e-b1e5-481f-bdc3-29928a357c95, abstract = {{From a control design point of view, modern diesel engines are dynamic, nonlinear, MIMO systems. This paper presents a method to find low-complexity black-box dynamic models suitable for model predictive control (MPC) of NOx and soot emissions based on on-line emissions measurements. A four-input-five-output representation of the engine is considered, with fuel injection timing, fuel injection duration, exhaust gas recirculation (EGR) and variable geometry turbo (VGT) valve positions as inputs, and indicated mean effective pressure, combustion phasing, peak pressure derivative, NOx emissions, and soot emissions as outputs. Experimental data were collected on a six-cylinder heavy-duty engine at 30 operating points. The identification procedure starts by identifying local linear models at each operating point. To reduce the number of dynamic models necessary to describe the engine dynamics, Wiener<br/><br> models are introduced and a clustering algorithm is proposed. A resulting set of two to five dynamic models is shown to be able to predict all outputs at all operating points with good accuracy.}}, author = {{Henningsson, Maria and Ekholm, Kent and Strandh, Petter and Tunestål, Per and Johansson, Rolf}}, booktitle = {{Proceedings of the 2010 American Control Conference}}, isbn = {{978-1-4244-7427-1}}, issn = {{2378-5861}}, keywords = {{Diesel engines; System identification; Wiener models}}, language = {{eng}}, pages = {{3015--3020}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{American Control Conference. Proceedings}}, title = {{Dynamic Mapping of Diesel Engine through System Identification}}, url = {{http://dx.doi.org/10.1109/ACC.2010.5531242}}, doi = {{10.1109/ACC.2010.5531242}}, year = {{2010}}, }