Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Smart Scheduling of Electric Vehicles Based on Reinforcement Learning

Version 1 : Received: 3 March 2022 / Approved: 7 March 2022 / Online: 7 March 2022 (09:20:13 CET)

A peer-reviewed article of this Preprint also exists.

Viziteu, A.; Furtună, D.; Robu, A.; Senocico, S.; Cioată, P.; Remus Baltariu, M.; Filote, C.; Răboacă, M.S. Smart Scheduling of Electric Vehicles Based on Reinforcement Learning. Sensors 2022, 22, 3718. Viziteu, A.; Furtună, D.; Robu, A.; Senocico, S.; Cioată, P.; Remus Baltariu, M.; Filote, C.; Răboacă, M.S. Smart Scheduling of Electric Vehicles Based on Reinforcement Learning. Sensors 2022, 22, 3718.

Journal reference: Sensors 2022, 22, 3718
DOI: 10.3390/s22103718

Abstract

Abstract: As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs at a larger scale. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data.

Keywords

Smart scheduling; Smart Reservations; Reinforcement Learning; Electric vehicle charging; Electric Vehicle Charging Management platform; DQN Reinforcement Learning algorithm

Subject

ENGINEERING, Automotive Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.

We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.