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Reinforcement Learning based Charging Station Management for Electric Vehicles

Authors:

Jiae Lee,

Zhamila Issimova,

Hyuk LimAuthors Info & Claims

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

Pages 450 - 452

https://doi.org/10.1145/3208903.3212057

Published: 12 June 2018 Publication History

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Abstract

Electric vehicles (EV) are emerging as a promising transportation medium because they provide better energy efficiency and are more environmentally friendly compared to gasoline-fueled vehicles. However, long charge times and low charger coverage hinder EV charging services. To overcome these problems, this paper proposes a reinforcement learning (RL) based optimal management policy that can maximize the utilization of EV chargers while guaranteeing quality of service. The proposed RL based scheme learns the arrival pattern of EVs and adjusts the service area of each charging station in a dynamic environment.

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Richard S. Sutton and Andrew G. Barto. 2017. Reinforcement Learning: An Introduction (2nd ed.). The MIT Press.

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Cited By

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Kang DYoon SLim H(2023)Deep Reinforcement Learning-based Building Energy Management using Electric Vehicles for Demand Response2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)10.1109/ICAIIC57133.2023.10066975(375-377)Online publication date: 20-Feb-2023
https://doi.org/10.1109/ICAIIC57133.2023.10066975
Chen SJiang CLi JXiang JXiao W(2021)Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial ParksEntropy10.3390/e2310131123:10(1311)Online publication date: 6-Oct-2021
https://doi.org/10.3390/e23101311
Yan LLiu WJiang WLi YLi RHu S(2021)Deep Reinforcement Learning based Optimization of Battery Charging and Discharging Management for Data Center2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533476(1-9)Online publication date: 2021
https://doi.org/10.1109/IJCNN52387.2021.9533476

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Published In

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

June 2018

657 pages

ISBN:9781450357678

DOI:10.1145/3208903

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2018

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Poster
Research
Refereed limited

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e-Energy '18

Sponsor:

SIGCOMM

e-Energy '18: The Ninth International Conference on Future Energy Systems

June 12 - 15, 2018

Karlsruhe, Germany

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Upcoming Conference

E-Energy '25

Sponsor:
sigenergy

The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

Rotterdam , Netherlands

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Cited By

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Kang DYoon SLim H(2023)Deep Reinforcement Learning-based Building Energy Management using Electric Vehicles for Demand Response2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)10.1109/ICAIIC57133.2023.10066975(375-377)Online publication date: 20-Feb-2023
https://doi.org/10.1109/ICAIIC57133.2023.10066975
Chen SJiang CLi JXiang JXiao W(2021)Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial ParksEntropy10.3390/e2310131123:10(1311)Online publication date: 6-Oct-2021
https://doi.org/10.3390/e23101311
Yan LLiu WJiang WLi YLi RHu S(2021)Deep Reinforcement Learning based Optimization of Battery Charging and Discharging Management for Data Center2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533476(1-9)Online publication date: 2021
https://doi.org/10.1109/IJCNN52387.2021.9533476

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