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A vehicle simulation model, CO 2 MPAS supports the introduction of the new WLTP-based certification system for CO 2 emissions in Europe. This paper investigates the possibility to use the underlying simulation methodology to accurately calculate CO 2 emissions over real-world trips, thus to extend the use of the methodology beyond vehicle certification. As a reference, the analysis used measurement data obtained from four vehicles over two different routes under real-world driving conditions. The CO 2 emissions were measured using portable emissions measurement systems. The formal CO 2 MPAS methodology and two modified versions of it that require a reduced number of input data were assessed about their capacity to predict the measured CO 2 emissions. The analysis focused on the accuracy and uncertainty of the three different methodology configurations. As an additional benchmark, the analysis considered the CO 2 emissions estimates obtained from the EMEP/EEA Guidebook methodology used for emissions inventorying in Europe. Results show that the basic CO 2 MPAS configuration demonstrates good performance in predicting CO 2 emissions over on-road tests, reaching a prediction accuracy over an entire test trip of −0,3% and a standard deviation of 3,1%. The modified versions showed slightly higher biases up to 3% and uncertainties (5–7%), but remaining within reasonable limits considering the reduced number of inputs used in each case. Given its ability to predict CO 2 emissions accurately on a local base, CO 2 MPAS could be used for the prediction of instantaneous CO 2 emissions in traffic micro-simulation exercises. [ABSTRACT FROM AUTHOR]

The study proposes a methodology for quantifying the impact of real-world heterogeneous driving behavior on vehicle energy consumption, linking instantaneous acceleration heterogeneity and CO 2 emissions. Data recorded from 20 different drivers under real driving are benchmarked against the Worldwide Harmonized Light Vehicle Test Cycle (WLTC), first by correlating the speed cycle with individual driver behavior and then by quantifying the CO 2 emissions and consumption. The vehicle-Independent Driving Style metric (IDS) is used to quantify acceleration dynamicity, introducing driving style stochasticity by means of probability distribution functions. Results show that the WLTC cycle assumes a relatively smooth acceleration style compared to the observed ones. The method successfully associates acceleration dynamicity to CO 2 emissions. We observe a 5% difference in the CO 2 emissions between the most favourable and the least favourable case. The intra-driver variance reached 3%, while the inter-driver variance is below 2%. The approach can be used for quantifying the driving style induced emissions divergence. [ABSTRACT FROM AUTHOR]