Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO₂ emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO₂ emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.

Measurement of car engines exhaust pollutants emissions is very important because of their harmful effects on the environment. This article presents the assessment of repeatability of the passenger car engine exhaust pollutants emission research results obtained in the conditions of a chassis dynamometer. The research was conducted in a climate chamber, enabling the temperature conditions to be determined from − 20 to + 30 °C. The emission of CO, CH₄, CO₂, NO_X, THC, and NMHC was subjected to the analysis. The aim of the research is to draw attention to the accuracy of the pollutant emission research results in driving cycles, and the comparison of pollutant emission results and their repeatability obtained in successive NEDC cycles under cold and hot start conditions. The results of the analysis show that, in the case of a small number of measurements, the results repeatability analysis is necessary for a proper interpretation of the pollutant emission results on the basis of the mean value. According to the authors’ judgment, it is beneficial to determine the coefficient of variation for a more complete assessment of exhaust emission result repeatability obtained from a small number of measurements. This parameter is rarely presented by the authors of papers on exhaust components emission research.