Vehicles with Internal Combustion Engines (ICE) still represent the most prevalent form of road transport in Europe, being an important source of both greenhouse gases and air pollutants. In response to these concerns, Portable Emission Measurement Systems (PEMS) have been widely used by researchers to measure tailpipe emissions and to detect cheating of emissions regulations by manufacturers. This paper introduces four different approaches to estimate carbon dioxide (CO2) and nitrogen oxides (NOx) emissions for these vehicles. These approaches were based on: i) speed intervals (≤50 km.h-1, 50-90 km.h-1, ≥ 90 km.h-1); ii) internally observable variables (IOVs); iii) vehicle specific power (VSP); and iv) driving volatility indicators. The development of IOVs models was made by testing the most significant parameters on CO2 and NOx emission rates, which included the engine revolution per minute (RPM), manifold absolute pressure (MAP), and intake air temperate (IAT). VSP-modal approach centred on binning emission rates in 14 models that reflects deceleration, idling, cruise, and acceleration states. Driver volatility was characterized by means of vehicular jerk (i.e., first derivate of acceleration) using nine combinations of vehicular jerk types. To obtain real world emissions, data were collected from one petrol and one diesel passenger cars using an integrated PEMS. IOVs and jerk models based on the product of MAP and RPM presented similar CO2 compared to measured values for both vehicles, but they resulted in higher overestimation of NOx than a VSP-modal approach. The proposed methodology can be extended to other individual ICE or alternative fuel vehicles for which it may be expensive to get emissions, engine, and dynamic data. ; published


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    Titel :

    Comparing different approaches for estimating tailpipe emissions in passenger cars


    Beteiligte:

    Erscheinungsdatum :

    01.01.2022


    Anmerkungen:

    2352-1465



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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