Learning from the Fleet: Map Attributes for Energetic Representation of Driving Profiles


More electric vehicles are necessary to reach the goals set by international greenhouse gas legislation. However, low range, long charging time, and a sparse infrastructure compared to fuel-based individual mobility reduce customer acceptance of individual electric mobility. Energetic planning functions as range prediction can ease dealing with those drawbacks. These predictive functions need detailed information on energy demand along the route. To enable this prediction, we present a novel analytical method to energetically represent velocity profiles along a map's link by four parameters. This method has 63 % less error in energetic driving profile representation than other methods. We present a framework to aggregate the four parameters from geolocation records provided by a fleet of different vehicles. We analyze this framework concerning the energetic error introduced to range estimation. The analysis shows that the errors of the framework are lower or of equal magnitude than the ones introduced to range estimation by recent residual high-voltage-battery energy estimation algorithms. A precise route energy demand prediction based on the presented findings is the basis for a more reliable range prediction. This prediction will help to increase the acceptance of individual electric mobility.