Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip

Abstract

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail.