Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.
This content will become publicly available on October 1, 2025
Understanding the Perception Differences of Charging Infrastructure among Electric Vehicle (EV) and Non-EV Users: a Network Analysis Perspective
In this study, we raise the concern that current understandings of user perceptions and
decision-making processes may jeopardize the sustainable development of charging
infrastructure and wider EV adoption. This study addresses three main concerns: (1)
most research focuses solely on battery electric vehicle users, neglecting plug-in
hybrid (PHEV) and non-EV owners, thus failing to identify common preferences or
transitional perceptions that could guide an inclusive development plan; (2) potential
factors influencing charging station selection, such as the availability of nearby
amenities and the role of information from social circles and user reviews, are often
overlooked; and (3) used methods cannot reveal individual items' importance or
uncover patterns between them as they often combine or transform the original items.
To address these gaps, we conducted a survey experiment among 402 non-EV, PHEV
and EV users and applied network analysis to capture their charging station selection
decision-making processes. Our findings reveal that non-EV and PHEV users prioritize
accessibility, whereas EV owners focus on the number of chargers. Furthermore,
certain technical features, such as vehicle-to-grid capabilities, are commonly
disregarded, while EV users place significant importance on engaging in amenities
while charging. We also report an evolution of preferences, with users shifting their
priorities on different types of information as they transition from non-EV and PHEV to
EV ownership. Our results highlight the necessity for adaptive infrastructure strategies
that consider the evolving preferences of different user groups to foster sustainable
and equitable charging infrastructure development and broader adoption of EVs.
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- Award ID(s):
- 2323732
- PAR ID:
- 10550539
- Publisher / Repository:
- 2025 Transportation Research Board Annual Meeting
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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