Gerard W. Malaczynski

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 preprocessing 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. INTRODUCTION Theoretical foundations linking the Ionsense signal with engine thermodynamics and combustion kinetics were laid by a group of scientists at Lund Institute of Technology, Sweden [1, 2]. The attempt, however, fell short of producing a robust theoretical model since the modeling of molecule formation and destruction kinetics in combustion, even if simplified, is extremely complex (see e.g. [3]). Consequently, the numerous attempts to demonstrate Ionsense “advanced functionality”, i.e. the ability to provide information on the location of peak pressure, air to fuel ratio, percentage of mass fraction burned, etc., proved to be successful only in a narrow range of automotive engine operating conditions [1 – 7] (leading papers are quoted here only). As was pointed out in numerous publications, fuel type, fuel additives [8], and fluctuation in early flame development [9] made the interpretation of the Ionsense signal extremely difficult. Remembering that air humidity, spark plug performance, engine aging, etc. may also affect the combustion process; a straightforward interpretation of the Ionsense signal appears to be almost impossible. Consequently, conventional analytical methods surely do not provide the robustness expected for mass production applications. In an attempt to solve this formidable problem, Halmstad University scientists together with Mecel, an independent subsidiary of Delphi Corporation, proposed the application of artificial neural networks (ANN) for Ionsense data interpretation (see e.g.: [10 12]). Indeed, the statistical fluctuations of combustion are well handled by the trained ANN-based Ionsense sensor which was demonstrated in experiments described by the above mentioned research group [11, 12]. Clearly, a very comprehensive ANN training covering a broad range of possible engine operating conditions would assure the correct data interpretation, at least, in the statistical sense, enough to enhance the performance of the engine control system. 2003-01-1119 Real-Time Digital Signal Processing of Ionization Current for Engine Diagnostic and Control Gerard W. Malaczynski and Michael E. Baker Delphi Corporation, Technical Center Brighton Copyright