Dynamic Mapping of Diesel Engine through System identification
Henningsson, Maria LU ; Ekholm, Kent LU ; Strandh, Petter LU ; Tunestål, Per LU and Johansson, Rolf LU
(2012)
Workshop on Identification for Automotive Systems
LNCIS 418.
p.223-239
- 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/3806347
- author
- Henningsson, Maria
LU
; Ekholm, Kent
LU
; Strandh, Petter
LU
; Tunestål, Per
LU
and Johansson, Rolf
LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Combustion, Engine, Control, Diesel, System Identification
- host publication
- Identification for Automotive Systems
- editor
- Alberer, D. ; Hjalmarsson, H. and del Re, L.
- volume
- LNCIS 418
- pages
- 223 - 239
- publisher
- Springer
- conference name
- Workshop on Identification for Automotive Systems
- conference location
- Linz, Austria
- conference dates
- 2010-07-15
- external identifiers
-
- wos:000306990500013
- scopus:84855882995
- ISSN
- 0170-8643
- DOI
- 10.1007/978-1-4471-2221-0_13
- project
- Competence Centre for Combustion Processes
- Diesel HCCI in a Multi-Cylinder Engine
- language
- English
- LU publication?
- yes
- id
- d7716619-3feb-489b-9353-0bcb1d1337f8 (old id 3806347)
- date added to LUP
- 2016-04-01 13:34:34
- date last changed
- 2023-02-21 23:08:23
@inproceedings{d7716619-3feb-489b-9353-0bcb1d1337f8,
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 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 = {{Identification for Automotive Systems}},
editor = {{Alberer, D. and Hjalmarsson, H. and del Re, L.}},
issn = {{0170-8643}},
keywords = {{Combustion; Engine; Control; Diesel; System Identification}},
language = {{eng}},
pages = {{223--239}},
publisher = {{Springer}},
title = {{Dynamic Mapping of Diesel Engine through System identification}},
url = {{https://lup.lub.lu.se/search/files/3453505/3806366.pdf}},
doi = {{10.1007/978-1-4471-2221-0_13}},
volume = {{LNCIS 418}},
year = {{2012}},
}