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Dynamic Mapping of Diesel Engine through System identification

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Author

Editor

  • D. Alberer
  • H. Hjalmarsson
  • L. del Re

Summary, in English

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.

Publishing year

2012

Language

English

Pages

223-239

Publication/Series

Identification for Automotive Systems

Volume

LNCIS 418

Document type

Conference paper

Publisher

Springer

Topic

  • Control Engineering
  • Other Mechanical Engineering

Keywords

  • Combustion
  • Engine
  • Control
  • Diesel
  • System Identification

Conference name

Workshop on Identification for Automotive Systems

Conference date

2010-07-15

Conference place

Linz, Austria

Status

Published

Project

  • Competence Centre for Combustion Processes
  • Competence Centre for Combustion Processes
  • Diesel HCCI in a Multi-Cylinder Engine

Research group

  • KCFP

ISBN/ISSN/Other

  • ISSN: 0170-8643