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A Virtual Sensor for Predicting Diesel Engine Emissions from Cylinder Pressure Data

In English

Författare

Summary, in English

Cylinder pressure sensors provide detailed information on the diesel engine combustion process. This paper presents a method to use cylinder-pressure data for prediction of engine emissions by exploiting data-mining techniques. The proposed method uses principal component analysis to reduce the dimension of the cylinder-pressure data, and a neural network to model the nonlinear relationship between the cylinder pressure and emissions. An algorithm is presented for training the neural network to predict cylinder-individual emissions even though the training data only provides cylinder-averaged target data. The algorithm was applied to an experimental data set from a six-cylinder heavy-duty engine, and it is verified that trends in emissions during transient engine operation are captured successfully by the model.

Publiceringsår

2012

Språk

Engelska

Sidor

424-431

Publikation/Tidskrift/Serie

IFAC Proceedings Volumes

Volym

45

Issue

30

Dokumenttyp

Konferensbidrag

Ämne

  • Control Engineering

Conference name

2012 IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM'12)

Conference date

2012-10-23 - 2012-10-25

Conference place

Rueil-Malmaison, France

Status

Published

Projekt

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

Forskningsgrupp

  • KCFP
  • LCCC

ISBN/ISSN/Övrigt

  • ISSN: 1474-6670
  • ISBN: 978-3-902823-16-8