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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 In 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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Combustion, Engine, Control, Diesel, System Identification
in
Identification for Automotive Systems
editor
Alberer, D.; Hjalmarsson, H.; del Re, L.; ; and
volume
LNCIS 418
pages
223 - 239
publisher
Springer
conference name
Workshop on Identification for Automotive Systems
external identifiers
  • wos:000306990500013
  • scopus:84855882995
ISSN
0170-8643
DOI
10.1007/978-1-4471-2221-0_13
project
Competence Centre for Combustion Processes
language
English
LU publication?
yes
id
d7716619-3feb-489b-9353-0bcb1d1337f8 (old id 3806347)
date added to LUP
2015-05-05 15:36:44
date last changed
2017-01-01 05:44:42
@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},
  keyword      = {Combustion,Engine,Control,Diesel,System Identification},
  language     = {eng},
  pages        = {223--239},
  publisher    = {Springer},
  title        = {Dynamic Mapping of Diesel Engine through System identification},
  url          = {http://dx.doi.org/10.1007/978-1-4471-2221-0_13},
  volume       = {LNCIS 418},
  year         = {2012},
}