Panousakis, Dimosthenis and Gazis, Andreas and Paterson, Jill and Chen, Wen-Hua and Chen, Rui and Turner, Jamie and Milovanovic, Nebosja (2006) Ion current signal interpretation via artificial neural networks for gasoline HCCI control. In: Society of Automotive Engineers World congress 2006, Cobo Centre, Detroit, MI, USA.
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|Item Type:||Conference or Workshop contribution (Presentation)|
|Item Status:||Live Archive|
The control of Homogeneous Charge Compression Ignition (HCCI) (also known as Controlled Auto Ignition (CAI)) has been a major research topic re- cently, since this type of combustion has the poten- tial 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 in- terpretation techniques such as artificial neural net- works 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 com- mercially 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 con- trol of the engine. Peak pressure position can be estimated with sufficient precision for practical ap- plications, thus pushing the HCCI operation closer to its limits.
|Keywords:||HCCI Combustion, Ion Current Sensing, Artificial Neural Networks|
|Subjects:||H Engineering > H330 Automotive Engineering|
G Mathematical and Computer Sciences > G730 Neural Computing
H Engineering > H311 Thermodynamics
|Divisions:||College of Science > School of Engineering|
|Deposited On:||05 Apr 2010 23:42|
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