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

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TOSUN E., AYDIN K., MEROLA S. S., IRIMESCU A.

THERMAL SCIENCE, vol.21, no.1, pp.401-412, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 21 Issue: 1
  • Publication Date: 2017
  • Doi Number: 10.2298/tsci160302151t
  • Journal Name: THERMAL SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.401-412
  • Çukurova University Affiliated: Yes

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 backpropagation 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. 

This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artcial 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 artcial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.