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An experimental study of the performance of a reverse tumble, DISI engine is reported. Specific fuel consumption and engine-out emissions have been investigated for both homogeneous and stratified modes of fuel injection. Trends in performance with varying AFR, EGR, spark and injection timings have been explored. It is shown that neural networks can be trained to describe these trends accurately for even the most complex case of stratified charge operation with exhaust gas recirculation.

Engine Electronic Control (EEC) systems on spark ignition engines enable a high degree of performance optimisation to be achieved through strategy and calibration details in software, but development times and costs can be high. The range of functions performed by EEC systems, and the level of performance demanded, are increasing and new methods of development are required. In the paper, the use of neural networks in the development and implementation of open-loop control of air/fuel ratio during engine transient operating conditions is described. The investigation has addressed the definition of suitable networks, the procedure and data required to train these, and assessment of real-time performance of the implemented system. The potential benefits of the approach include reduced calibration effort and simplification of the control strategy.