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

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

Abstract

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.

Details

Authors
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Control Engineering
Original languageEnglish
Title of host publication2012 IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling
PublisherIFAC
Pages424-431
ISBN (Print)978-3-902823-16-8
Publication statusPublished - 2012
Publication categoryResearch
Peer-reviewedYes
Event2012 IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM'12) - Rueil-Malmaison, France
Duration: 2012 Oct 232012 Oct 25

Publication series

Name
ISSN (Print)1474-6670

Conference

Conference2012 IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM'12)
CountryFrance
CityRueil-Malmaison
Period2012/10/232012/10/25

Total downloads

No data available

Related projects

Per Tunestål, Maria Henningsson & Rolf Johansson

Project: ResearchCollaboration with industry

View all (1)