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1. Understanding the Perception Differences of Charging Infrastructure among Electric Vehicle (EV) and Non-EV Users: a Network Analysis Perspective

   Gazmeh, Hossein ; Hamim, Omar ; Reimer, Torsten ; Loaiza-Ramírez, Pablo ; Ukuusuri, Satish ; Todd, Peter ; Jones, Steven ; Qian, Xinwu ( October 2024 , 2025 Transportation Research Board Annual Meeting)

   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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2. Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation

   Li, C. ; Dong, Z. ; Fisher, N. ; Zhu, D. ( March 2023 , Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022)

   Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus on centralized strategies to achieve optimized resource allocation, particularly useful for privacy-indifferent taxi fleets and fixed-route public transits. However, private EV driver seeks a more personalized and resource-aware charging recommendation that is tailor-made to accommodate the user preference (when and where to charge) yet sufficiently adaptive to the spatiotemporal mismatch between charging supply and demand. Here we propose a novel Regularized Actor-Critic (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods. 

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3. Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation

   Li, Chengyin ; Dong, Zheng ; Fisher, Nathan ; Zhu, Dongxiao ( March 2023 , Machine Learning and Knowledge Discovery in Databases)

   Amini, MR ; Canu, S. ; Fischer, A. ; Guns, T. ; Kralj Novak, P. ; Tsoumakas, G. (Ed.)

   Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus on centralized strategies to achieve optimized resource allocation, particularly useful for privacy-indifferent taxi fleets and fixed-route public transits. However, private EV driver seeks a more personalized and resource-aware charging recommendation that is tailor-made to accommodate the user preference (when and where to charge) yet sufficiently adaptive to the spatiotemporal mismatch between charging supply and demand. Here we propose a novel Regularized Actor-Critic (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods. 

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4. A Reliability-aware Distributed Framework to Schedule Residential Charging of Electric Vehicles

   https://doi.org/10.24963/ijcai.2022/710  

   Meyur, Rounak ; Thorve, Swapna ; Marathe, Madhav ; Vullikanti, Anil ; Swarup, Samarth ; Mortveit, Henning ( July 2022 , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence)

   Residential consumers have become active participants in the power distribution network after being equipped with residential EV charging provisions. This creates a challenge for the network operator tasked with dispatching electric power to the residential consumers through the existing distribution network infrastructure in a reliable manner. In this paper, we address the problem of scheduling residential EV charging for multiple consumers while maintaining network reliability. An additional challenge is the restricted exchange of information: where the consumers do not have access to network information and the network operator does not have access to consumer load parameters. We propose a distributed framework which generates an optimal EV charging schedule for individual residential consumers based on their preferences and iteratively updates it until the network reliability constraints set by the operator are satisfied. We validate the proposed approach for different EV adoption levels in a synthetically created digital twin of an actual power distribution network. The results demonstrate that the new approach can achieve a higher level of network reliability compared to the case where residential consumers charge EVs based solely on their individual preferences, thus providing a solution for the existing grid to keep up with increased adoption rates without significant investments in increasing grid capacity.

    

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5. Evaluating electric vehicle user mobility data using neural network-based language models

   Alvarez, K.* ( January 2019 , Proceedings of the Transportation Research Board (TRB) annual meeting)

   By displacing gasoline and diesel fuels, electric cars and fleets offer significant public health benefits by reducing emissions from the transportation sector. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine learning based on 12,720 U.S. electric vehicle charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the expanding population of EV drivers in 651 core-based statistical areas in the United States. Contrary to predictions, we find that stations at private charging locations do not outperform public charging locations provided by government. We also find evidence of higher negative sentiment in the dense urban centers, where issues of charge rage and congestion may be the most prominent. Overall, 40% of drivers using mobility apps have faced negative experiences at EV charging stations, a problem that needs to be fixed as the market expands. 

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