Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning framework to optimize the placement of electric vehicle charging stations, considering demand factors like traffic, EV registrations, and nearby public buildings, to improve infrastructure efficiency.

Contribution

It introduces a novel RL-based method for optimal EV charging station placement that accounts for multiple demand predictors, adaptable to different urban environments.

Findings

RL framework effectively predicts optimal station locations

Incorporates traffic, EV registrations, and public building proximity

Potential for global application in urban planning

Abstract

The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs). In Albany County in New York, the expected rise in the EV population requires additional charging stations to maintain a sufficient level of efficiency across the charging infrastructure. A novel application of Reinforcement Learning (RL) is able to find optimal locations for new charging stations given the predicted charging demand and current charging locations. The most important factors that influence charging demand prediction include the conterminous traffic density, EV registrations, and proximity to certain types of public buildings. The proposed RL framework can be refined and applied to cities across the world to optimize charging station placement.