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Title: Ion current signal interpretation via artificial neural networks for gasoline HCCI control
Authors: Panousakis, Dimosthenis
Gazis, Andreas
Paterson, Jill
Chen, Wen-Hua
Chen, Rui
Turner, James W.G.
Milovanovic, Nesa
Keywords: HCCI
Internal combustion engines
Neural networks
Issue Date: 2006
Publisher: © SAE International
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.
Series/Report no.: SAE Technical Paper;2006-01-1088
Abstract: 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.
Description: 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.
Version: Published
DOI: 10.4271/2006-01-1088
URI: https://dspace.lboro.ac.uk/2134/15630
Publisher Link: http://dx.doi.org/10.4271/2006-01-1088
ISBN: 9780768017212
0768017211
Appears in Collections:Conference Papers and Presentations (Aeronautical and Automotive Engineering)

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