SUMMARY

As the effects of climate change become increasingly severe, car manufacturers are facing more pressure from the public to offer alternatives to gasoline-powered cars such as Battery Electrical Vehicles (BEVs). As the number of BEVs grows, so do the need for more charge stations to fuel these vehicles. Using documented traffic patterns from electrical vehicles, this thesis aims to simulate BEV drivers’ behavior within Germany by using optimal route planning and implementing queuing theory.
In these simulations, optimal route planning provides each of the drivers their ideal route and charging plan to maximize battery range and improved comfort. On the other hand, queuing theory helps elevate the realism of the simulations by modeling human behavior. By using these methods, real traffic data of German BEV drivers can be modeled within a Multi Agent Transport Simulation tool and then analyzed. The results show that with the increase of BEV population, the demand of certain charge stations grows uncontrollably, leading to bottlenecks in certain areas of Germany. However, using queuing theory to simulate drivers' queuing behavior not only significantly reduces average wait times but also enhances the simulation's realism, mirroring real-world scenarios. More realistic simulations of BEVs are needed to not only predict how these vehicles interact with one another but also to accurately plan for the demand of charge stations. To conclude, the thesis reveals that the traffic patterns of BEV users could lead to long queue times at certain charge stations in real-world scenarios.