Konzeption und Implementierung einer streckenbasierten Fahrsimulationsumgebung zur Vorauslegung elektrischer Fahrzeugantriebe

Kurzfassung

The electrification of the powertrain has transitioned from a strategic consideration to a mandatory requirement. The design of an electric traction machine necessitates a load profile that accurately reflects its intended operating conditions. However, standardized cycles such as the WLTC provide only an averaged, rather than a specific, usage profile. This thesis develops a route-based driving simulation environment that generates a load cycle from any real-world route and derives the requirements for the preliminary design of electric machines. Starting from an origin and destination address, a routing service supplies the point sequence, elevation profile, road classes and speed limits, which are supplemented by traffic light positions from OpenStreetMap. A look-ahead driver model derives a target speed profile from these data, which a longitudinal vehicle model with limited tractive force then follows. The tool is complemented by a vehicle database, a driving cycle database and a graphical user interface. Validation is conducted in two stages: first, against seven measured drives of an Opel Corsa Electric on a 76 km commuting route, and second, against the WLTC. The simulation accurately reproduces speed profiles, power, and speed requirements, while the net energy demand is underestimated by approximately 15%. The primary causes are identified and quantified as auxiliary loads not represented in the model and recuperation potential calculated without a power limit. Peak and continuous power, torque and speed ranges as well as energy demand can be derived from the load cycle. The distribution of operating points shows that the machine works predominantly at low torque and high speed. The recuperation share amounts to 8 % on the real route but 23 % in the WLTC, demonstrating that characteristic values derived from standardised cycles cannot readily be transferred to real usage profiles.