Browse Publications Technical Papers 2022-01-1108
2022-08-30

Methodology to Estimate Fuel Consumption of Heavy-Duty Vehicles Through the Use of Two Different Fuel Formulations 2022-01-1108

Nowadays, real-world emissions and consumption behaviour of Light Duty (LDV) and Heavy Duty (HDV) vehicles are key factors in achieving greenhouse gas (GHG) targets. With the introduction of EURO VI in 2013 there were already low emission levels and real fuel consumption of new HDV vehicles. Furthermore, the available public literature regarding fuel consumption of European HDV vehicles is not very extensive. Hence, the development of an experimental activity related to HDVs real consumption measurement and the subsequent data analysis can be considered in this field. To this end, the fuel consumption data of four rear-loader garbage Diesel trucks, managed by a multiservice company in the Southern Italy, were collected during real use. Vehicles in pairs have different technical characteristics (i.e. engine capacity and maximum load capacity of the garbage). In this paper, we describe the methodology implemented to analyze the complete set of data (collected through questionnaires) from this experimental activity as well as the main results produced from this analysis. The whole road trials lasted six months. During this period, vehicles performed their real use and, in cross pairs, they were refueled with the two different commercial fuels, alternating them after three months of experimentation. In addition, the vehicles were equipped with GPS to track their performance during service and collect the data necessary to carry out an evaluation of the mileage travelled and an estimate of the average speed. The applied methodology is implemented through statistical methods to detect outliers and the two-sample t-test procedure to check whether there are significant differences between the mean fuel consumption during the reference period. Methodology basically involves the standardization of data set, ensuring that the comparison is statistically significant.

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