How to Reduce Range Anxiety? The Impact of Digital Elevation Model Quality on Energy Estimates for Electric Vehicles

摘要

Reliable energy estimation methods are a very important step to addressing the range anxiety problem of electric vehicle adoption. Besides driving patterns and vehicle parameters, geographic information about elevation changes is one of the most important pieces of information to predict energy consumption. This paper presents a method to assess the impact of digital elevation model (DEM) quality on energy consumption estimation for electric vehicle routes. We demonstrate the use of this method by applying it to compare energy consumption estimates for 16,500 randomly generated routes, based on three recently released open DEM datasets: NASA Shuttle Radar Topographic Mission (SRTM) version 3.0, EU-DEM, and open government DEM data provided by the city of Vienna. Results show that energy consumption models tend to overestimate route energy consumption by a mean error of 2.9% and 15.8%, respectively, when lower-resolution DEMs are used to compute route elevation profiles. A spatial analysis of the error distribution shows that the mean error varies between different regions within the analysis area, with bigger error values in the hills and in the city centre indicating that high-resolution elevation data is not only important in hilly and mountainous areas, but also in dense urban environments.