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

Mohammad Al Ja'idi, Nauman Aslam, Xiaomin Chen, Kaiwartya Omprakash, Yousef Ali Al-Gumaei, Muhammad Khalid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
7 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2022 IEEE 95th Vehicular Technology Conference
Subtitle of host publication(VTC2022-Spring) Proceedings
Place of PublicationPiscataway, US
PublisherIEEE
Pages147-153
Number of pages7
Volume1
ISBN (Electronic)9781665482431
ISBN (Print)9781665482448
DOIs
Publication statusPublished - Jun 2022
EventThe 2022 IEEE 95th Vehicular Technology Conference: VTC2022-Spring - Scandic Grand Marina Congress Center and Hotel, Helsinki, Finland
Duration: 19 Jun 202222 Jun 2022
https://events.vtsociety.org/vtc2022-spring/

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Conference

ConferenceThe 2022 IEEE 95th Vehicular Technology Conference
Country/TerritoryFinland
CityHelsinki
Period19/06/2222/06/22
Internet address

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