A GIS-based optimal facility location framework for fast electric vehicle charging stations

Deeper decarbonization of the transport sector requires building a wide coverage electric vehicle charging network that can meet driver's mobility patterns and refueling habits in a
seamless manner. Currently, major market players mainly deploy chargers at existing public parking spaces at hotels, shopping centers, etc. On the other hand, gas/petroleum retail business is a century-old industry and "optimized" to serve the refueling needs of the drivers and they come to the forefront as "good" locations to site chargers. To that end, this paper addresses the fast charging station location problem in an urban environment. The optimization problem is formulated as a maximum coverage location problem (MCLP) and existing locations of petrol/fuel stations are considered as candidate locations. Using QGIS
software, a geographic information system (GIS) based platform is developed and integrated with a linear-programming relaxation based MCLP algorithm developed in Python. The city of Raleigh, North Carolina with actual geo-spatial data is chosen as a case study. Both census population and highway traffic data are considered as demand metrics to mimic drivers without dedicated chargers and vehicles on highways who need a recharge. A number of evaluations are performed to explore the trade-off between the number of locations and the physical coverage space. Furthermore, comparative analysis show that locating fast
chargers in existing petrol stations improve demand coverage by more than 50% when compared to existing fast charging station locations.