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

TL;DR

This paper presents a neural network-based method to predict individual electric vehicle charging station usage from physical network configurations, enabling faster and more efficient network design without extensive simulations.

Contribution

It introduces a neural network model that estimates station usage based on physical placement, aiding rapid design optimization of EV charging networks.

Findings

Neural network accurately predicts station usage from configurations.

Method reduces the need for computationally expensive simulations.

Supports rapid testing of different network layouts.

Abstract

Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks. We use neural networks to predict individual charging station usage statistics from the station's physical location within a network. We have shown this quickly gives accurate estimates of average usage statistics given a proposed configuration, without the need for running many computationally expensive simulations. The trained neural network can help EV charging network designers rapidly test various placements of charging stations under additional individual constraints in order to find an optimal configuration given their design objectives.