A Simulation Study of Electric Vehicle Traffic Patterns in Germany using Route Planning and Queuing Theory
Author(s)
Sharma, Deeksha
Advisor(s)
Sawodny, Oliver
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Abstract
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 and reduce the queues of these drivers by using optimal route planning in conjunction with a large-scale agent-based transport simulation framework, while also implementing queuing theory.
In these simulations, route planning algorithm 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 can reduce average wait times. Moreover, this thesis also simulates futuristic scenarios, where the charging infrastructure is improved. These result in further shortened queue times.
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 simulated BEV drivers lead to long queue times at certain charge stations when traveling on real traffic routes scenarios, but can be reduce with the implementation of a better charging infrastructure.
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Date
2023-12-05
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