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In this paper the behavior of the three-way catalyst equipping a 2-liter car is fully analyzed considering exhaust gas and ceramic temperatures trends ( and of related engine parameters) in on-road operations of the car under different traffic conditions. These are classified by means of driving cycles clusters determined by multivariate statistical analysis of car speed and gear usage real profiles, detected on-road by the same car in designed experimental plans. Instantaneous fuel consumption and signals of a linear oxygen sensor, placed up-stream catalyst, have been analyzed to better characterize engine and catalyst performance. Emissions are measured in laboratory performing the most statistically representative driving cycles with car on the dynamometer chassis. The effect of traffic conditions on catalyst behavior and exhaust emissions is analyzed through the study of series of consecutive driving cycles.

A system for both ignition and ion current measurement was designed and set up at Istituto Motori. Particular attention was paid to the problem of dissipating the residual energy stored in the ignition coil, reducing the electromagnetic interferences and especially improving the response of the measurement system. In order to assess the capability of the ion current signal to give reliable and accurate information for knock detection, a number of tests were carried out at full load on a commercial PFI four cylinder engine, at various air/fuel ratios and spark timings. Some knock indices based on the ionization signal, both band pass filtered and non-filtered, were introduced, in particular: the Amplitude of the Second Ionization Peak (ASIP), the Mean not filtered Ionization Current signal (MIC), the Maximum Amplitude of Ionization Current signal Oscillation (MAICO), the Integral of Modulus of filtered Ionization Current signal Oscillation (IMICO).

Transport activities contribute significantly to air pollution. For this reason any policy or plan, carried out by administration or institution, requires the assessment of its impact on the emissions. To assess the overall pollutant production from transport, it is necessary to calculate emission factors. For this aim several methods exist which only use the average speed of the traffic stream, which can be theoretically obtained by vehicles flow and density on the road. Recently, a new statistical approach has been developed capable to consider more attributes than the simple mean speed to characterize driving behaviour, not only in the determination of driving cycles but also in the emission modelling. In this context, a meso scale emission model, named KEM, Kinematic Emission Model, able to calculate emission factor was developed. However, it is necessary to consider that the input to this model is, in any case, the driving cycle.

A novel model has been developed for the analysis and the evaluation of average vehicle emissions in a real driving cycle (emission factors) from data in an emission database. The model assumes that emission variation can be explained by parameters determined from dynamic vehicle equation and by the frequency of acceleration events at different speeds. Because the number of resulting X-variables is large, and variables are correlated, a regression method based on principal components, the Partial Least Squares (PLS) method actually, has been adopted. In this paper, model potentiality is illustrated by an application to a case study taken from the database built within the UE V Framework Project ARTEMIS. Data are relative to tests performed under hot conditions with a sample of EURO III 1.4-2.0 l gasoline passenger cars. A set of real driving cycles was utilized as representative of urban, rural and motorway operating conditions detected in different European countries.