Affiliation:
1. Higher School of Communication of Tunis (Sup’Com), University of Carthage, 2083 Ariana, Tunisia
2. Chair of Distributed Information Systems, University of Passau, Innstraße 41, 94032 Passau, Germany
Abstract
The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.
Funder
Open Access Publication Fund of University Library Passau
German Federal Ministry for Digital and Transport with the Project OMEI
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference19 articles.
1. IEA (2023, September 30). Global EV Outlook 2023. Available online: https://www.iea.org/energy-system/transport/electric-vehicles.
2. Thiel, C., Alemanno, A., Scarcella, G., Zubaryeva, A., and Pasaoglu, G. (2012). Attitude of European car drivers towards electric vehicles: A survey, JRC Report.
3. Reinforcement learning based EV charging management systems—A review;Abdullah;IEEE Access,2021
4. Constrained EV charging scheduling based on safe deep reinforcement learning;Li;IEEE Trans. Smart Grid,2019
5. Effective charging planning based on deep reinforcement learning for electric vehicles;Zhang;IEEE Trans. Intell. Transp. Syst.,2020