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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 (2010) American Control Conference, 2010 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:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Diesel engines, System identification, Wiener models
pages
3015 - 3020
conference name
American Control Conference, 2010
external identifiers
  • WOS:000287187903066
  • Scopus:77957757802
language
English
LU publication?
yes
id
44d7617e-b1e5-481f-bdc3-29928a357c95 (old id 1759171)
date added to LUP
2011-01-05 09:41:45
date last changed
2016-10-13 04:59:12
@misc{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},
  keyword      = {Diesel engines,System identification,Wiener models},
  language     = {eng},
  pages        = {3015--3020},
  title        = {Dynamic Mapping of Diesel Engine through System Identification},
  year         = {2010},
}