Ion current sensing for controlled auto ignition in internal combustion engines

Envirom-nental pollution is a subject that needs urgent addressing. Since the internal combustion engine has its fair share of accountability on this, research on techniques for increasing engine efficiency and emissions is necessary. Controlled Auto Ignition is a promising combustion mode, which increases fuel efficiency while also reducing NOx emissions to negligible levels. This Thesis concentrates on the implementation of this mode through experimental research, on an engine equipped with a fully variable valvetrain. Investigation of the operational window, emissions, fuel consumption, thermodynamic efficiency is carried out and ways to improve on these are discussed. The governing consideration, however, is the control method for this rather intricate combustion mode. As such, experimental data acquisition and analysis of ion current under the whole operating spectrum, from spark ignition to full autoignition is made. It is found that the expected gains in fuel consumption and emissions are realized. In addition, ion current proves to be a very powerful and cost effective tool for engine monitoring, diagnosis and control. The author concludes that Controlled Auto Ignition is a viable proposition for mass production engine designs and that ion current, although not absolutely vital for engine control, considerably increases engine control thus allowing for greater operating window under autoignition, without compromising reliability or cost.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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

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

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

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