Analysis of SI combustion diagnostics methods using ion-current sensing techniques

Closed-loop electronic control is a proven and efficient way to optimize spark ignition engine performance and to control pollutant emissions. In-cylinder pressure sensors provide accurate information on the quality of combustion. The conductivity of combustion flames can alternatively be used as a measure of combustion quality through ioncurrent measurements. In this paper, combustion diagnostics through ion-current sensing are studied. A single cylinder research engine was used to investigate the effects of misfire, ignition timing, air to fuel ratio, compression ratio, speed and load on the ion-current signal. The ion-current signal was obtained via one, or both, of two additional, remote in-cylinder ion sensors (rather than by via the firing spark plug, as is usually the case). The ion-current signals obtained from a single remote sensor, and then the two remote sensors are compared. Ion-current signal interpretation was then conducted using an artificial neural network strategy (using adaptive linear networks) to interpret the measured signals, and also to predict the associated cylinder pressures. The combination of remote sensors with a linear neural network gives a more accurate and ‘noise’ free signal that can be processed at greater speed through computationally inexpensive methods. The computed results agree well with measured cylinder pressures under all analyzed conditions. It will be shown that ion-current signals can be used to directly diagnose combustion abnormalities (and as such could suitable as part of a closed loop control strategy), even though the effects of ignition timing, air to fuel ratio, and compression ratio on ion-current were more complex.