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In order to meet the stricter and stricter emission regulations, cleaner combustion concepts for Diesel engines are being progressively introduced. These new combustion approaches often requires closed loop control systems with real time information about combustion quality. The most important parameter for the evaluation of combustion quality in internal combustion engines is the in-cylinder pressure, but its direct measurement is very expensive and involves an intrusive approach to the cylinder. Previous researches demonstrated the direct relationship existing between in-cylinder pressure and engine block vibration signal and several authors tried to reconstruct the pressure cycle on the basis of information coming from accelerometers mounted on engine block. This paper proposes a method, based on the analysis of the engine vibration signal, for the diagnosis of combustion process in a Diesel engine.

Use of EGR (Exhaust Gas Recirculation) and after-treatment devices allows diesel engines to comply with actual emission regulations. In order to satisfy future emission standards it will be necessary a careful analysis of peculiarities and limits of the current systems for pollution control and of their possible influence on production of other harmful substances. Engine control maps determine optimal EGR considering a trade-off between NOx and smoke emissions. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle number density, which has a great importance for the optimal functioning of after-treatment systems. In this paper a soft computing model that gives real time information on the characteristic of exhaust particles, is proposed. The model, by using a neural network approach, is able to provide information on the effect of EGR on particulate mass concentration and particle size distribution.

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.

A PC-programmable electronic control unit (PECU), able to manage both conventional and future electronic injection systems to make a fixed number of consecutive injections (1 to 5 or more) controlling the injection pressure and the injection pulses duration as well as the separation time or dwell in between was used to study the behaviour of a Bosch common rail injection system both on dynamic spray bench and on engine test bench. The PECU allowed a reduction in the dwell time between consecutive injection pulses from the current value of 1800 μs to 500 μs. Photographic sequences of a five holes mini-sac nozzle making five consecutive injections at 400 - 800 and 1200 bar respectively were taken at ambient pressure and temperature. They showed that both spray penetration and cone angle at all operative conditions are very uniform and stable.

Increasing demands on emissions reduction and efficiency encouraged a progressive introduction of cleaner combustion concepts. "Advanced" diesel combustions offer a high potential for simultaneous reduction of both NOx and soot within the engine through high inlet charge dilution and mixture homogenization. However, the potential benefits of these combustions in terms of emissions are counterbalanced by their high sensitivity to in-cylinder thermodynamic conditions. This sensitivity makes the engines require closed loop combustion control with real-time information about combustion quality. The parameter widely considered as the most important for the evaluation of the combustion quality in internal combustion engines is the cylinder pressure. However, this kind of measure involves an intrusive approach to the cylinder, expensive sensors and a special mounting process.