THERMAL SCIENCE

International Scientific Journal

ESTIMATION OF OPERATIONAL PARAMETERS FOR A DIRECT INJECTION TURBOCHARGED SPARK IGNITION ENGINE BY USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORK

ABSTRACT
This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.
KEYWORDS
PAPER SUBMITTED: 2016-03-02
PAPER REVISED: 2016-05-07
PAPER ACCEPTED: 2016-06-08
PUBLISHED ONLINE: 2016-07-12
DOI REFERENCE: https://doi.org/10.2298/TSCI160302151T
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2017, VOLUME 21, ISSUE 1, PAGES [401 - 412]
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© 2017 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence