Fuzzy neural ignition timing control for a natural gas fuelled spark ignition engine

Abstract

One of the important inputs to a spark ignition engine which affects nearly all engine outputs is ignition timing. It is well known that the optimum ignition timing which gives the maximum brake torque for a given engine design varies with the rate of flame development and propagation in the cylinder. Modern engines show ignition timing being generally controlled by fixed open-loop schedules as functions of engine speed and load. It is desirable that this ignition timing can be adjusted to the optimum level which produces the best torque to obtain minimum fuel consumption and maximum available power. This paper presents an ignition timing control system based on fuzzy logic and neural network theories. A fibre optical sensor system was developed for measurement of the intensity of the luminous emission which correlates the combustion pressure and ignition timing control on a Ford 1600 cm3 four-cylinder spark ignition engine fuelled with natural gas. Several engine tests were carried out in optimizing the combustion intensity detection system. The results obtained provide important information compatible with intelligent control of the engine using fuzzy neural control technology. Moreover, tests carried out with data using this technology show good results that fit quite well with the original engine output torque characteristics.