Fuel consumption prediction for heavy-duty vehicles using digital maps

With the steep increase of heavy-duty transportation, more fuel efficient technologies and services have become of great importance due to their environmental impact (in terms of CO2 emissions) and economical impact (costs for the fleet managers). This work addresses the problem of finding the less expensive itinerary among several ones provided by a Geographic Information System (GIS). The problem focuses on the prediction of a speed profile as realistic and as representative of the route as possible and physically feasible for a truck. The novelty of the proposed tool lies on the precise understanding of the road as well as on the study of the vehicle's parameters impact on the fuel consumption. This work is of great interest for a truck fleet manager wanting to quantify the fuel cost of the trip's external conditions while varying for instance the truck's cargo.