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Research project within TUM CREATE. Focussing on modelling and optimisation of architecture and infrastructure, urban systems simulation like traffic and power are the main research interests. Apart from that, a cognitive systems group deal with human computer interaction. 


A Simulation-based Heuristic for City-scale Electric Vehicle Charging Station Placement

Jiajian Xiao

Electric Vehicles (EVs) play an important role towards a more sustainable transportation system. Sufficient charging infrastructure is, however, needed in order to accommodate their power demand and increase EV adoption. In this study, a simulation-based approach for charging station (CS) placement using an agent-based traffic simulation is proposed. The heuristic's objective is to achieve sufficient network coverage to keep charging related inconvenience within an acceptable range while minimising the overall number of CSs. For this purpose, the algorithm identifies locations at which the charging procedure seamlessly integrates into the drivers' itineraries, thus minimising detours and waiting times. At the same time, the algorithm attempts to maximise the utilisation of each CS throughout the day in order to minimise the number of CSs. The methodology is demonstrated at the example of Singapore. The investigation shows that the charging demand of 20,000 EVs can be covered with approximately 2,500 CSs by accepting average detours no greater than 410 metres and average waiting time below 10 minutes. This number can be further reduced by relaxing the inconvenience criterion. 

The main components of the proposed methodology are a charging behaviour model and a CS placement algorithm. The first one determines under what conditions a driver decides to recharge the vehicle’s battery, the latter aims for placing CSs in a way which best suits the drivers’ charging needs.

For charging behaviour, two types of charging behaviours are defined depending on the battery usage:

  • Mandatory Charging: charge when battery is not sufficient

At the end of each trip, an estimation is made to check if the remaining energy is enough to support the next trip + some safety margin.  And mandatory charging is triggered when the remaining energy is less than the estimation

We introduce a queuing behaviour. When a mandatory charge is required and the CS is currently occupied, a vehicle will queue up for charging. We assume an unlimited queue for each CS.

  • Convenience Charging: charge during parking

Convenience charging is modelled by considering a trade-off between remaining the energy and the distance to the nearest CS. 

CS placement algorithm removes unused CS and merges nearby CSs with less usage.

The combined usage of two CSs is computed by the following formula. If the combined usage is below a certain threshold, these two CS will be combined.


And the new CS will be created at the place which uses the idea of centre of mass

We run the experiment with CityMoS. The best result we achieved is:

With 20,000 EVs, we achieve a distribution of CSs as illustrated by the figure. 

  • Approximately 2,500 CSs
  • Average detour 410 meters
  • Average queuing time of 10 minutes
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