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Ion current signal interpretation via artificial neural networks for gasoline HCCI control

conference contribution
posted on 2014-08-21, 08:21 authored by Dimosthenis Panousakis, Andreas Gazis, Jill Paterson, Wen-Hua ChenWen-Hua Chen, Rui Chen, James W.G. Turner, Nesa Milovanovic
The control of Homogeneous Charge Compression Ignition (HCCI) (also known as Controlled Auto Ignition (CAI)) has been a major research topic recently, since this type of combustion has the potential to be highly efficient and to produce low NOx and particulate matter emissions. Ion current has proven itself as a closed loop control feedback for SI engines. Based on previous work by the authors, ion current was acquired through HCCI operation too, with promising results. However, for best utilization of this feedback signal, advanced interpretation techniques such as artificial neural networks can be used. In this paper the use of these advanced techniques on experimental data is explored and discussed. The experiments are performed on a single cylinder cam-less (equipped with a Fully Variable Valve Timing (FVVT) system) research engine fueled with commercially available gasoline (95 ON). The results obtained display an improvement in the correlation between characteristics of ion current and cylinder pressure, thus allowing superior monitoring and control of the engine. Peak pressure position can be estimated with sufficient precision for practical applications, thus pushing the HCCI operation closer to its limits.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

SAE Technical Papers

Citation

PANOUSAKIS, D. ... (et al.), 2006. Ion current signal interpretation via artificial neural networks for gasoline HCCI control. Presented at: 2006 SAE World Congress, Detroit, USA, 3-6 April.

Publisher

© SAE International

Version

  • VoR (Version of Record)

Publication date

2006

Notes

Copyright © 2006 SAE International. This paper is posted on this site with permission from SAE International. It may not be shared, downloaded, duplicated, printed or transmitted in any manner, or stored on any additional repositories or retrieval system without prior written permission from SAE.

ISBN

9780768017212;0768017211

Book series

SAE Technical Paper;2006-01-1088

Language

  • en

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