SI engine simulation using residual gas and neural network modeling to virtually estimate the fuel composition

K.Y. Chan , Andrzej Ordys , O. Duran , Konstantin Volkov , J. Deng

Abstract

Research in electronic controlled internal combustion engines mainly focuses on improving performance and lowering the emissions. Combustion performance depends on the geometry of cylinders and on the design of all mechanical parts, which are based on the laboratory experimental research. Due to the limitations of the materials used in the engine and the continuous high operating temperature, engines function in either spark ignition or charge ignition processes. Recent research on computer controlled engines uses sensors and electronic actuators which allows switching the engine operational mode between spark ignition and charge ignition. Thus, this makes possible to mix intake fuel compositions in order to give more choices to consumers.
This study employs a neural network which is capable of estimating fuel composition using the parameters of residual gas. The simulation is based on a thermodynamic engine model implemented in Matlab Simulink. The main advantages are the capabilities of the model to 1) calculate the gas exchange as a function of time in transient mode, and 2) to generate data for the design control algorithms without the need of the engine bed test environment to test various fuel compositions.
Author K.Y. Chan - [Kingston University]
K.Y. Chan,,
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, Andrzej Ordys (FM / IACR) - [Kingston University]
Andrzej Ordys,,
- The Institute of Automatic Control and Robotics
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, O. Duran - [Kingston University]
O. Duran,,
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, Konstantin Volkov - [Kingston University]
Konstantin Volkov,,
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, J. Deng - [Kingston University]
J. Deng,,
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Pages897-903
Publication size in sheets0.5
Book Ao S.I., Douglas Craig, Grudnfest W. S., Burgstone Jon (eds.): Proceedings of the World Congress on Engineering and Computer Science 2013, Lecture Notes in Engineering and Computer Science, vol. 2, 2013, Newswood Limited, ISBN 978-988-19253-1-2
Keywords in EnglishMatlab, neural-network, SI-engine, simulation
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 10.0, 14-05-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 14-05-2020, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators Scopus Citations = 1; WoS Citations = 1; GS Citations = 4.0
Citation count*4 (2020-09-13)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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