Time Optimal Routing of Electric Vehicles Under Consideration of Available Charging Infrastructure and a Detailed Consumption Model

While battery electric vehicles (EVs) are on the advance, the broad customer basis is still concerned about battery electric range, a phenomenon commonly known as range anxiety. To tackle these concerns, the functionality of in-vehicle navigational systems must adapt to the new propulsion technology with a limited battery capacity. Central aim is to consider charging infrastructure in route planning. Furthermore, detailed powertrain models are required to accurately forecast an EV's energy consumption. On the other hand, such detailed models are hardly applicable to large scale road networks that are usually handled by routing services for vehicle navigation. This study proposes a two-staged approach to compute time optimal routes for EVs. To this end, a reduced road network is obtained from a leading routing service. Subsequently, a detailed consumption model is applied and the resulting multiobjective shortest path problem is solved using an adapted Moore-Bellman-Ford algorithm. Within an experimental study, the consumption forecast is validated against measurement data and query times of the proposed methodology are assessed for generic routing problems. The former shows significant improvement of consumption forecast accuracy compared to state-of-the-art models while the latter indicates potential for application in car manufacturers vehicle backend services.