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An experimental study has been carried out to explore what limits fuel injection and EGR strategies when trying to run a PCCI-type mode of combustion on an engine with current generation hardware. The engine is a turbocharged V6 DI diesel with (1600 bar) HPCR fuel injection equipment and a cooled external EGR system. The variables examined have been the split and timings of fuel injections and the level of EGR; the responses investigated have been ignition delay, heat release, combustion noise, engine-out emissions and brake specific fuel consumption. Although PCCI-type combustion strategies can be effective in reducing NOx and soot emissions, it proved difficult to achieve this without either a high noise or a fuel economy penalty.

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.