A Reinforcement Learning-based Assignment Scheme for EVs to Charging Stations

Aljaidi, Mohammad, Aslam, Nauman, Chen, Xiaomin, Omprakash, Kaiwartya, Al-Gumaei, Yousef Ali Mohammed and Khalid, Muhammad (2022) A Reinforcement Learning-based Assignment Scheme for EVs to Charging Stations. In: 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring). IEEE, Piscataway, US, pp. 1-7. (In Press)

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Abstract

Due to recent developments in electric mobility, public charging infrastructure will be essential for modern transportation systems. As the number of electric vehicles (EVs) increases, the public charging infrastructure needs to adopt efficient charging practices. A key challenge is the assignment of EVs to charging stations in an energy efficient manner. In this paper, a Reinforcement Learning (RL)-based EV Assignment Scheme (RL-EVAS) is proposed to solve the problem of assigning EV to the optimal charging station in urban environments, aiming at minimizing the total cost of charging EVs and reducing the overload on Electrical Grids (EGs). Travelling cost that is resulted from the movement of EV to CS, and the charging cost at CS are considered. Moreover, the EV’s Battery State of Charge (SoC) is taken into account in the proposed scheme. The proposed RL-EVAS approach will approximate the solution by finding an optimal policy function in the sense of maximizing the expected value of the total reward over all successive steps using Q-learning algorithm, based on the Temporal Difference (TD) learning and Bellman expectation equation. Finally, the numerous simulation results illustrate that the proposed scheme can significantly reduce the total energy cost of EVs compared to various case studies and greedy algorithm, and also demonstrate its behavioural adaptation to any environmental conditions.

Item Type: Book Section
Additional Information: 2022 IEEE 95th Vehicular Technology Conference: VTC2022-Spring; Helsinki, Finland, 19-22 Jun 2022
Uncontrolled Keywords: Electric vehicle assignment, charging station, Q-learning, temporal difference, Bellman expectation equation, energy consumption, energy cost, electrical grids
Subjects: G400 Computer Science
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: John Coen
Date Deposited: 08 Jun 2022 15:01
Last Modified: 09 Jun 2022 08:00
URI: http://nrl.northumbria.ac.uk/id/eprint/49270

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