Real-Time Digital Signal Processing of Ionization Current for Engine Diagnostic and Control

Combustion quality diagnostic techniques utilizing flame ionization measurement, with the spark plug as a sensor, have been in production for some time. This acquired “Ionsense” signal represents the changes in the electrical conductivity of the flame during each combustion event. The present analog versions of this sensor are used to detect knock and engine misfire, and can be used for cam phasing. However, current methodology has fallen short of unlocking the wealth of combustion thermodynamics information encrypted in the ion sense signal.

Digital Signal Processing incorporating Artificial Neural Networks (ANN) is well suited for handling the statistical fluctuations of combustion. However to obtain acceptable accuracy, traditional ANN implementations can require processing resources beyond the capability of current engine controllers. Using Air/Fuel Ratio and Location of Peak Pressure as examples, this paper explores the practicality of performing real-time digital processing of the Ionsense signal to extract additional combustion information. An assessment of required processor resources is made and alternative pre-processing employing a pattern recognition wavelet filter is proposed. As a result the post-processed signal seems to be immune to some engine combustion fluctuations not included in the ANN training.

The concepts discussed were successfully demonstrated throughout the normal operating range, in real-time, on a 6-cylinder engine. Examples of performance data are included.