A transport simulation-based analysis of optimal long-distance route planning for battery electric vehicles in Germany

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Weist, Matthew
Fedorov, Andrei G.
Sawodny, Oliver
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To reduce their carbon footprint and fight climate change, German car manufacturers are transitioning from internal combustion engines 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 route and charging plan that achieves the fastest total trip time. In this paper, the impacts on trip time of the individualized optimization of BEV routing in a large-scale network are explored. A dynamic programming algorithm is employed, which considers vehicle, road, and charging station parameters to determine the fastest routing and charging combination. To explore the large-scale impact of such optimization, several scenarios are simulated using a Multi Agent Transport Simulation model of Germany. The results show that average queue times at charging stations can be measurably reduced when the algorithm considers the plug count at each charging station. Additionally, futuristic scenarios are simulated in which improvements are made to charging and range technology. These results show a large decrease in average queue time, but long queues still occur at select charging stations. In conclusion, the large-scale usage of a common, individual-based optimal routing algorithm may lead to long queue times at particular charging stations. The strategic placement of charging infrastructure and the advancement in BEV technology can markedly reduce queue times and total trip times, leading to a more practical experience for BEV drivers of the future.
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