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In order to meet the increasingly strict emission regulations, several solutions for NOx and PM emissions reduction have been studied. Exhaust gas recirculation (EGR) technology has become one of the more used methods to accomplish the NOx emissions reduction. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle size and density. These latter have a great importance both for the optimal functioning of after-treatment systems, but also for the adverse effects that small particles have on human health. Epidemiological studies, in fact, highlighted that the toxicity of particulate particles increases as the particle size decreases. The aim of this paper is to present a Neural Network model able to provide real time information about the characteristics of exhaust particles emitted by a Diesel engine.

Gasoline direct injection (GDI) combustion with un-throttled lean stratified operation allows to reduce engine toxic emissions and achieve significant benefits in terms of fuel consumption. However, use of gasoline stratified charges can lead to several problems, such as a high cycle-to-cycle variability and increased particle emissions. Use of multiple injection strategies allows to mitigate these problems, but it requires the injection of small fuel amounts forcing the traditional solenoid injectors to work in their “ballistic” region, where the correlation between coil energizing time and injected fuel amount becomes highly not linear. In the present work a closed-loop control system able to manage the delivery of small quantities of fuel has been introduced. The control system is based on a particular feature found on the coil voltage command signal during the de-energizing phase.