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dc.contributor.authorMunir, Md. Shirajumen_US
dc.contributor.authorKim, Ki Taeen_US
dc.contributor.authorThar, Kyien_US
dc.contributor.authorNiyato, Dusiten_US
dc.contributor.authorHong, Choong Seonen_US
dc.identifier.citationMunir, M. S., Kim, K. T., Thar, K., Niyato, D. & Hong, C. S. (2022). Risk adversarial learning system for connected and autonomous vehicle charging. IEEE Internet of Things Journal, 9(16), 15184-15203.
dc.description.abstractIn this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and autonomous vehicles (AVs). To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data informed manner. We propose a novel risk adversarial multiagent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. The experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE’s active charging time, and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.en_US
dc.relation.ispartofIEEE Internet of Things Journalen_US
dc.rights© 2022 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleRisk adversarial learning system for connected and autonomous vehicle chargingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.subject.keywordsConnected and Autonomous Vehicleen_US
dc.subject.keywordsIntelligent Transportation Systemsen_US
dc.description.acknowledgementThis work was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) under Grant 2020R1A4A1018607; in part by the IITP Grant funded by MSIT (Evolvable Deep Learning Model Generation Platform for Edge Computing) under Grant 2019-0-01287; and in part by the IITP Grant funded by the Korea Government (MSIT, Artificial Intelligence Innovation Hub) under Grant 2021-0-02068.en_US
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