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The Real Driving Emission (RDE) procedure will measure the pollutants, such as NOx, emitted by cars while driven on the road. RDE will not replace laboratory tests, such as the current WLTP but it will be added to them. RDE is complementary to the laboratory-based procedure to check the pollutant emissions level of a light-duty vehicle in real driving conditions. This means that the car will be driven on a real road according to random acceleration and deceleration patterns conditioned by traffic flow. So, the procedure will ensure that cars deliver real emissions over on-road and so the currently observed differences between emissions measured in the laboratory and those measured on road under real-world conditions, will be reduced. However, the identification of a path on the road to check the test conditions of RDE is not easy and hardly repeatable.

A numerical investigation is performed with the aim of understanding the potential benefits of multiple injections in the mixed mode boosting operation of a Gasoline Direct Injection (GDI) engine. The study is carried out by firstly characterizing a high pressure multi-hole injector from the experimental point of view in the split injection operation. Measurements of the fuel injection rate are made through an AVL Meter operating on the Bosch principle. The injector is tested using gasoline in a double pulse strategy. The injection pressure is varied between 5.0 and 25.0 MPa with the pulse durations calibrated for delivering a total mass up to 50 mg/str. The choice of the dwell time between two successive injection events is achieved by firstly defining the minimum time compatible with the mechanical characteristics of both the injector and the injector driver.

In recent years, great interest on NVH characteristics of vehicles has been paid by all the big automotive manufacturers. Interior acoustic comfort is now one of the main key factors in vehicle development process, since it contributes to improved product overall quality. Therefore, in automotive industry advanced NVH refinement needs to work in synergy with all research activities. Assessing the level of experienced noise in interior cabin requires particular arrangements for ensuring adequate measurement accuracy (AC system off, closed window, etc.). The use of parameters such as the level of seat vibration, not affected by the acoustic field conditions inside the vehicle, could facilitate experiments in parallel with engine/vehicle calibration activities.

In the context of a transport sustainability, some solutions could be proposed from the integration of many disciplines, architects, environmentalists, policy makers, and consequently it may be addressed with different approaches. These solutions would be applied at different geographical levels, i.e. national, regional or urban scale. Moreover, the assessment of cars emissions in real use plays a fundamental role for their reductions. This is also the direction of the new harmonized test procedures (WLTP). Furthermore, it is fundamental to keep in mind that the new WLTC cycle will reproduce a situation closer to the reality comparing to the EUDC/NEDC driving cycle. In this paper, we will be focused on vehicle kinematic evaluation aimed at valuation of traffic situation and emissions.

This study aims at building efficient and robust artificial neural networks (ANN) able to reconstruct the in-cylinder pressure of Diesel engines and to identify engine conditions starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the artificial neural network is meant to be efficient in terms of response time, i.e. fast enough for on-line use. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. Here we consider a feed-forward neural network based on radial basis functions (RBF) for signal reconstruction, and a feed-forward multi-layer perceptron network with tan-sigmoid transfer function for signal classification. The networks are trained using measurements from a three-cylinder real engine for various operating conditions.