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Electric vehicles (EVs) have been gaining popularity due to theirenvironmental friendliness and efficiency. EV charging station networks arescalable solutions for supporting increasing numbers of EVs within modernelectric grid constraints, yet few tools exist to aid the physicalconfiguration design of new networks. We use neural networks to predictindividual charging station usage statistics from the station's physicallocation within a network. We have shown this quickly gives accurate estimatesof average usage statistics given a proposed configuration, without the needfor running many computationally expensive simulations. The trained neuralnetwork can help EV charging network designers rapidly test various placementsof charging stations under additional individual constraints in order to findan optimal configuration given their design objectives.

Predicting Electric Vehicle Charging Station Usage: Using Machine  Learning to Estimate Individual Station Statistics from Physical  Configurations of Charging Station Networks