Ion Current Signal Interpretation via Artificial Neural Networks for Gasoline HCCI Control

Dimosthenis Panousakis; Andreas Gazis; Jill Paterson; Wen-Hua Chen; Rui Chen; Jamie Turner; Nebosja Milovanovic

doi:10.4271/2006-01-1088

2006-04-03

Ion Current Signal Interpretation via Artificial Neural Networks for Gasoline HCCI Control 2006-01-1088

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.

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Ion Current Signal Interpretation via Artificial Neural Networks for Gasoline HCCI Control 2006-01-1088

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