SUMMARY

To reduce their carbon footprint and fight climate change, German car manufacturers are transitioning from internal combustion engines (ICEs) to Battery Electric Vehicles (BEVs). Although modern BEVs have amply sized batteries for short-distance trips, long-distance trips require the usage of a currently adolescent public charging infrastructure. To accelerate Germany’s transition to a majority-BEV market, car manufacturers are implementing strategies to maximize battery range and alleviate customer range-anxiety. One such strategy is optimal route planning: providing the driver with the routing and charging plan that achieves the fastest total trip time. In this paper, the optimization of BEV routing in a largescale network is explored by combining an optimization algorithm with a transport simulation model of Germany. For the optimization, a dynamic programming algorithm is employed, which considers vehicle, road, and charging station parameters to determine the fastest routing and charging combination. Several scenarios are simulated to explore the impact and efficacy of such optimization. The results show a decrease in total trip time when compared to standard BEV driver behavior. Waiting times occur at popular charging stations, but the waiting time is reduced when the algorithm considers the plug count at each charging station. In conclusion, optimal route planning reduces the overall trip time, which could ease customers’ transition to BEVs. Additionally, the large-scale simulation of long-distance EV travel helps to identify bottlenecks in Germany’s charging infrastructure.