Dynamic Mapping of Diesel Engine through System identification

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


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.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Control Engineering
  • Other Mechanical Engineering


  • Combustion, Engine, Control, Diesel, System Identification
Original languageEnglish
Title of host publicationIdentification for Automotive Systems
EditorsD. Alberer, H. Hjalmarsson, L. del Re
VolumeLNCIS 418
Publication statusPublished - 2012
Publication categoryResearch
EventWorkshop on Identification for Automotive Systems - Linz, Austria
Duration: 2010 Jul 15 → …

Publication series

VolumeLNCIS 418
ISSN (Print)0170-8643


ConferenceWorkshop on Identification for Automotive Systems
Period2010/07/15 → …

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