Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164444
Title: Risk adversarial learning system for connected and autonomous vehicle charging
Authors: Munir, Md. Shirajum
Kim, Ki Tae
Thar, Kyi
Niyato, Dusit
Hong, Choong Seon
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Munir, 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. https://dx.doi.org/10.1109/JIOT.2022.3149038
Journal: IEEE Internet of Things Journal
Abstract: In 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.
URI: https://hdl.handle.net/10356/164444
ISSN: 2327-4662
DOI: 10.1109/JIOT.2022.3149038
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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