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

• URL: http://arxiv.org/abs/2108.07772v1
• Date: Tue, 17 Aug 2021 17:25:30 GMT
• Title: Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning
• Authors: Aidan Petratos, Allen Ting, Shankar Padmanabhan, Kristina Zhou, Dylan Hageman, Jesse R. Pisel, Michael J. Pyrcz
• Abstract summary: A novel application of Reinforcement Learning (RL) is able to find optimal locations for new charging stations. The proposed RL framework can be refined and applied to cities across the world to optimize charging station placement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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