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Adaptive neuro-fuzzy method to estimate virtual SI engine fuel composition using residual gas parameters

Chan, Kinyip, Ordys, Andrzej, Duran, Olga, Volkov, Konstantin and Deng, Jiamei (2014) Adaptive neuro-fuzzy method to estimate virtual SI engine fuel composition using residual gas parameters. In: 10th International Conference on Control (Control 2014); 9 - 11 Jul 2014, Loughborough.

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Abstract

This paper addresses computer engine simulation with an adaptive neuro fuzzy method to estimate the fuel composition by using the residual gas information. The availability of fuel composition is divided into. The actual ready mixed composition provided from suppliers, named in gasoline, contains different name hydro-carbon atoms. The composition is unknown, but varies with standard based on engine combustion profile. This study researches the idea of further engine control on fuel composition to improve engine performance and reduce on emissions. Fuel composition can be estimated using combustion product after gas exchange. This study investigates a computer based engine model which uses Adaptive Neuro-Fuzzy Interface System (ANFIS) to identify the fuel composition. The residual composition contains the level of Carbon Dioxide (CO2), Oxygen (O), Carbon Monoxide (CO) and Nitric Oxide (NO) which developed the network to estimate the Hydrocarbon level of original fuel input. Results show that ANFIS control is reasonably distinguish three different fuel compositions in the tests.

Item Type: Conference or Workshop Item (Paper)
Event Title: 10th International Conference on Control (Control 2014)
Organising Body: IEEE
Research Area: Electrical and electronic engineering
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing > School of Mechanical and Automotive Engineering
Depositing User: Olga Duran
Date Deposited: 23 Jul 2014 11:36
Last Modified: 20 Jan 2015 09:44
URI: http://eprints.kingston.ac.uk/id/eprint/28280

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