Analysis of pollutant emissions of a lorry for different EURO standards and operating conditions

Environmental pollution is becoming an increasingly important problem that needs to be solved, and road vehicles contribution in that pollution is significant. In that sense, in this paper, a brief overview of models used to determine pollutant emissions is given, and then the environmental pollution of an actual lorry with a maximum permissible mass of up to 7.5 t is specifically considered. While determining pollutant emissions different Euro standards, average vehicle speeds, payload utilizations and longitudinal road slopes were taken into account. Emissions of carbon monoxide (CO), nitrogen oxides (NOx) and particulate matter (PM) were observed in detail in this paper

Evaluation of the influence of terrain and traffic road conditions on the driver’s driving performances by applying machine learning

In this paper, research is done in the influence of different terrain and traffic conditions on road sections on the driver’s driving performances, i.e. on the car energy efficiency and CO2 emission. A methodology aimed at determining to which extent unfavorable traffic and/or terrain conditions on a road section contribute to the driver’s worse driving performances, and also to determine when the driver’s aggressive driving style is responsible for greater fuel consumption and greater CO2 emission is proposed. In order to apply the proposed methodology, a research study was carried out in a cargo transportation company and 12 drives who drove the same vehicle on five different road sections were selected. As many as 284014 of the instances of the data about the defined parameters of the road section and the driver’s driving style were collected, based on which and with the help of machine learning a prediction of the scores for the road section and the scores for the driver’s driving style was performed. The obtained results have shown that the proposed methodology is a useful tool for managers enabling them to simply and quickly determine potential room for increasing the energy efficiency of the vehicle fleet and decreasing CO2 emission