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1. Joint planning for battery swap and supercharging networks with priority service queues

   Zhang, J. Jin; Bai, L. (March 2021, International journal of production economics)

   null (Ed.)

   Existing network planning models for electric vehicle (EV) services usually treat the battery swap and the on-board supercharging as two independent processes. This study makes an early attempt to design an EV charging network where battery swap and supercharging are jointly coordinated. The swap and supercharge processes are characterized by Erlang B and Erlang C priority queues, respectively. A strategic location-allocation model is formulated to optimize the station sites, battery stock level, and the number of superchargers at chosen sites. Three design criteria, namely, battery state-of-charge, maximum service time, and power grid constraint, are simultaneously taken into account. Meta-heuristics algorithms incorporating Tabu search are developed to tackle the proposed non-linear mixed integer optimization model. Computational results on randomly generated instances show that the priority battery service scheme outperforms the pure battery swap station in terms of spare battery investment cost and charging flexibility. The case study on a real-world traffic network comprised of 0.714 million households further shows the efficacy and advantage of the dual battery charging process for ensuring state-of-charge, service time commitment, and network-wide grid stability. 

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2. 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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3. EMT-Model-Based HIL Testbed for Large Scale EV-Integrated Distribution Grids

   https://doi.org/10.1109/TPEC63981.2025.10906832  

   Yadav, Ankit; Hussain, Arif; Ravikumar, Gelli (February 2025, IEEE)

   Extreme fast chargers (XFCs) have enabled electric vehicle (EV) charging in less than 15 minutes. These charging units may draw up to 300 kW. An extreme fast charging station (XFCS), made of multiple XFCs, may draw up to a few MW. Grid-integration of these units for EV charging in grid-to-vehicle mode (G2V) mode presents significant power demand and operational challenges for the grid. Whereas in vehicle-to-grid (V2G) mode, they also present an opportunity to enable EV-based grid support services. Cyber vulnerabilities add an additional dimension to it. The present paper develops an electromagnetic transient (EMT)-model-based hardware-in-the-loop (HIL) testbed for large-scale EV-integrated distribution grids. It implements the IEEE 123 bus feeder model to simulate the distribution grid. The proposed testbed has four XFCS with a total of 12 XFC units, each integrated with one EV. This testbed provides flexibility in defining XFC and EV parameters to simulate and analyze the impact of integrating the wide spectrum of EV and XFC technologies. The case study presented in this paper considers a power rating of 300 kW, enabling 100% charging of a typical 50 kWh EV battery in approx 10 minutes. This paper also developed the mathematical model for four types of man-in-the-middle (MitM) cyberattacks on charging control and integrated them into the proposed testbed. Furthermore, the testbed is designed considering the scalability in terms of both charging units/stations as well as a variety of cyberattacks. The testbed is successfully developed and tested using the eMEGASIM platform of the Opal-RT real-time simulator. 

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4. Optimal Location of Electrical Vehicle Charging Stations With Both Self-and Valet-Charging Service

   https://doi.org/10.1109/TASE.2023.3321412  

   Zhao, Tianyi; Li, Na; Kong, Nan; Xie, Xiaoqing (January 2024, IEEE Transactions on Automation Science and Engineering)

   The inconvenience of charging is one of the major concern for potential electric vehicle (EV) users. In addition to building more charging facilities, electric vehicle charging assistance service has emerged for making EV charging more convenient to customers. In this paper, we consider an optimal EV charging station location problem with two types of customers. One is ordinary self-charging customers whereas the other is customers using a new service mode called valet-charging. We formulate the problem via bi-level location optimization model, where the lower level problem is a game model that characterizes customers’ station choice behaviors. To solve the hard nonlinear mixed-integer optimization problem, we design an adaptive large neighbourhood search (ALNS) algorithm for the upper level problem and a construct-improve heuristic for the lower level problem. We conduct numerical experiments to justify the efficiency of our solution method. We also conduct a need-inspired case study to derive practical insights which will help EV charging assistant service providers make strategic decisions. The convenience of charging service is one major concern for EVs. In China, NIO Inc., NETA AUTO, and FAW-Volkswagen have started to provide valet-charging service. Charging station location problem becomes complicated while taking this service into account. We believe our work develops an effective tool for charging station planners to analyze station locations as well as the impact of valet charging services. 

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5. Reinforcement Learning Based Fast Charging of Electric Vehicle Battery Packs

   https://doi.org/10.4271/2023-01-1681  

   Abbasi, Mohammad Hossein; Arjmandzadeh_PhD, Ziba; Zhang, Jiangfeng; Xu, Bin; Krovi, Venkat N (October 2023, Society of Automotive Engineers International)

   Range anxiety and lack of adequate access to fast charging are proving to be important impediments to electric vehicle (EV) adoption. While many techniques to fast charging EV batteries (model-based & model-free) have been developed, they have focused on a single Lithium-ion cell. Extensions to battery packs are scarce, often considering simplified architectures (e.g., series-connected) for ease of modeling. Computational considerations have also restricted fast-charging simulations to small battery packs, e.g., four cells (for both series and parallel connected cells). Hence, in this paper, we pursue a model-free approach based on reinforcement learning (RL) to fast charge a large battery pack (comprising 444 cells). Each cell is characterized by an equivalent circuit model coupled with a second-order lumped thermal model to simulate the battery behavior. After training the underlying RL, the developed model will be straightforward to implement with low computational complexity. In detail, we utilize a Proximal Policy Optimization (PPO) deep RL as the training algorithm. The RL is trained in such a way that the capacity loss due to fast charging is minimized. The pack’s highest cell surface temperature is considered an RL state, along with the pack’s state of charge. Finally, in a detailed case study, the results are compared with the constant current-constant voltage (CC-CV) approach, and the outperformance of the RL-based approach is demonstrated. Our proposed PPO model charges the battery as fast as a CC-CV with a 5C constant stage while maintaining the temperature as low as a CC-CV with a 4C constant stage. 

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