Wireless Power Transfer Systems: Design, Challenges, and Applications in EV Charging Infrastructure

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

<p>Charging infrastructure for electric vehicles (EV) will be the key factor for ensuring a smooth transition to e-mobility. This paper focuses on five technologies that will play a fundamental role in this regard: smart charging, vehicle-to-grid (V2G), charging of EVs from photovoltaic panels (PV), contactless charging and on-road charging of EVs. Smart charging of EVs is expected to enable larger penetration of EVs and renewable energy, lower the charging cost and offer better utilization of the grid infrastructure. Bidirectional EV chargers will pave the way for V2G technology where the EV can be used for energy arbitrage and demand-side management. Solar charging of EV will result in sustainable transportation and use of the EV battery as PV storage. On the other hand, stationary contactless charging and on-road inductive charging of EV will remove the necessity for any cables, eliminate range anxiety issues and pave the way for automated driving. The electromagnetic and power converter design for contactless power transfer systems for future highways is reviewed in this paper.</p>

Equitable and sufficient charging infrastructure is required for transport decarbonization to reach its goals. Despite increased electric vehicle infrastructure roll out rates, there is still considerable uncertainty regarding the charging market. For example, studies have evidenced disparities in electric vehicle charging placement, however, predictable as the market caters for early adopters. While there is an emerging discourse surrounding social equity in charging infrastructure, this is scattered across interdisciplinary research covering broader aspects of electric vehicle infrastructure provision with a lack of studies consolidating issues. This study aims to synthesize evidence on social equity in various aspects of electric vehicle charging infrastructure provision and set an agenda for centering social equity in the debate. Findings of this critical synthesis of research have helped to draw out the complexities involved in the equitable roll out of electric vehicle charging infrastructure, which are interlinked with an array of other dimensions including the affordability of electric vehicle purchase. Research into solutions and best practice has shown examples of local target setting, monetary incentives (grants, loans and rebates for electric vehicle purchase and charging infrastructure and smart energy tariffs) and other policy incentives (increased public overnight charging, electric car-clubs, extended battery warranties for second-hand vehicles) that can or have been employed to redress the balance. The outcomes could be utilized when developing and implementing electric vehicle strategies to support uptake across all people. Policy implications and further study suggested could ensure that communities and individuals are not locked out of the benefits of investment.

The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.

There is a growing penetration of electric vehicle (EV) charging infrastructure in the UK, but the increasing risk of flooding caused by climate change has not been sufficiently considered during the planning and operation stages. This paper develops a new spatial risk assessment framework to evaluate the flood exposure and spatial clustering of EV charging stations across England. A composite scoring system, integrating the geographic location of charging stations, elevation, river proximity, and government-defined flood zone classification, is built to rank individual chargers into six exposure levels. Local Moran’s I is applied to identify high-risk spatial clusters. The method: 1) categories Local Authority Districts (LADs) into four spatial risk types by using a quadrant-based framework and 2) identifies critical "blind spots" where flood vulnerability is high, but infrastructure coverage remains insufficient. This study reveals risk aggregation in major UK cities, and proposes resilient charging station deployment strategies, considering typologies and localized risk characteristics. The findings highlight the urgent need for flood-adaptive planning in future EV infrastructure development.

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

Electric vehicle (EV) charging infrastructure development is one of the key aspects of the electrification of transportation systems. However, incorporating EV public charging facilities has practical and financial concerns, especially in developing economies. Recently, mobile public charging stations (MPCS) have been considered an alternate, practical, commercially scalable solution. Present research on MPCS operation algorithms assumes centralized algorithm execution and does not offer any features related to consumer data security. Moreover, they do not exploit the multi-port charging facility of MPCS, which is more economical for scheduling services. In this paper, a comprehensive framework for the decentralized execution of the MPCS operation algorithm for EV charging service using a hybrid cloud-edge server architecture is proposed. The decentralized execution enhances EV data security and reduces load on the cloud server, data transmission, and storage charges. Four vehicle routing algorithms, including exact integer programming (IP), Greedy, Greedy+2Opt heuristic, and meta-heuristic Tabu search algorithms, are applied to test the efficacy of the proposed framework. From the studies, the proposed framework with Greedy+2Opt performed well concerning scalability. Compared to one-to-one MPCS-EV charging, the proposed one-to-many charging framework offered a significant reduction in the transportation cost and the required number of MPCSs for a given energy delivered. Further, for a given MPCS battery capacity and service time window, the framework delivered higher energy compared to one-to-one service model.

<div class="section abstract"><div class="htmlview paragraph">The rapid expansion of electric vehicle (EV) charging infrastructure introduces complex cybersecurity challenges across hardware, software, network, and cloud layers. This review paper synthesizes existing research, standards, and documented incidents to identify critical vulnerabilities and propose layered mitigation strategies. We present a structured threat taxonomy based on the STRIDE model, enriched with real-world attack vectors and mapped to mitigation controls. Our analysis spans physical tampering, insecure firmware updates, protocol-level flaws in OCPP and ISO 15118, and cloud misconfigurations. While prior studies often focus on isolated domains, this work unifies fragmented insights into a cohesive framework. We highlight gaps in current literature, such as inconsistent adoption of secure protocols and limited validation of EVSE identity formats. By aligning threats with industry standards (SAE J3061, NIST CSF, IEC 62443) and scoring risks using CVSS v3.1, we offer a practical roadmap for manufacturers, operators, and policymakers. The paper concludes with recommendations for future research, including experimental validation, blockchain-based audit trails, and AI-driven anomaly detection.</div></div>

An agent-based simulation study for escaping the “chicken-egg” dilemma between electric vehicle penetration and charging infrastructure deployment

Non-intrusive load monitoring (NILM) is an important topic in smart-grid and\nsmart-home. Many energy disaggregation algorithms have been proposed to detect\nvarious individual appliances from one aggregated signal observation. However,\nfew works studied the energy disaggregation of plug-in electric vehicle (EV)\ncharging in the residential environment since EVs charging at home has emerged\nonly recently. Recent studies showed that EV charging has a large impact on\nsmart-grid especially in summer. Therefore, EV charging monitoring has become a\nmore important and urgent missing piece in energy disaggregation. In this\npaper, we present a novel method to disaggregate EV charging signals from\naggregated real power signals. The proposed method can effectively mitigate\ninterference coming from air-conditioner (AC), enabling accurate EV charging\ndetection and energy estimation under the presence of AC power signals.\nBesides, the proposed algorithm requires no training, demands a light\ncomputational load, delivers high estimation accuracy, and works well for data\nrecorded at the low sampling rate 1/60 Hz. When the algorithm is tested on\nreal-world data recorded from 11 houses over about a whole year (total 125\nmonths worth of data), the averaged error in estimating energy consumption of\nEV charging is 15.7 kwh/month (while the true averaged energy consumption of EV\ncharging is 208.5 kwh/month), and the averaged normalized mean square error in\ndisaggregating EV charging load signals is 0.19.\n

Novel partial power converter topologies for renewable-integrated ultra-fast electric vehicle charging stations

The fast-expanding electric vehicle market requires efficient high-voltage charging methods. The documentation introduces a new PV-based switched inductor-switched capacitor (SL-SC) high voltage gain boost converter that addresses high-voltage EV charging requirements. The proposed converter design offers high voltage gain performance together with increased efficiency and diminished stress on its components. Recursive circuit analysis includes modeling of the circuit design alongside operational principle research and performance assessment. The simulation together with experimental studies prove that the proposed circuit design outperforms traditional boost converters concerning operational effectiveness. Efficient power converters that provide maximum voltage gain and intelligent control designs must be established because high-voltage electric vehicle (EV) charging systems need more effective solutions. This research explores the implementation of a high voltage gain boost converter with PV-supplied SL-SC topology which utilizes ANN control for high-voltage EV charging systems. SL-SC networks integrated through the proposed topology produce both high voltage conversion ratios and minimal semiconductor component stress that improves system performance and efficiency. The controller based on ANN optimizes the converter operation across different solar irradiation and load ranges by providing rapid system responses alongside improved voltage regulation. The proposed system proves better than conventional boost and coupled inductor-based converters through comparative analysis of voltage gain along with efficiency and transient response performance. Testing results together with simulation data support that this proposed structure represents an effective solution for future high-voltage systems which enable EV charging sustainability.

The adoption of Electric Vehicle (EV) can shave the peak load and flatten the load profile in an urban area. However, the uncontrolled patterns of simultaneous and randomised EV charging may increase the peak load and thus destabilise the power grid. To address this problem effectively, this paper proposes an optimal charging scheme with the objective of lowering the power fluctuation level. The charging scheme takes into account the uncertainty of EV's driver behaviour and EV charging demand model with minimal impacts on daily routine of EV users. Firstly, a power fluctuation level problem is formulated and a novel EV charging scheme based on Genetic Algorithm is further proposed to solve the problem. The simulation results have shown the robustness of the proposed scheme in lowering the power fluctuation level and the overall peak demand significantly in the grid system.

The rapid expansion of electric vehicles (EVs) has added complexity to the resilience and security challenges to the EV charging systems, especially owing to the exposure to the cyber–physical threats and the reliance on centrally coordinated systems. Although the previous literature has discussed the use of blockchain in the context of smart grids and mobility services; its implementation to improve the resilience of EV charging, particularly when integrated with cybersecurity systems, is still insufficiently synthesized. Despite these issues, critical gaps persist in terms of scalability, interoperability, and cybersecurity enforcement. This study presents an exploratory literature review that examines the intersection of blockchain and cybersecurity enabled applications and introduces a comparative framework evaluating the conventional security controls with blockchain based cybersecurity solutions to improve the resilience of EV charging infrastructure. The authors analyzed 70 studies published between 2018 and 2025 to determine the security weaknesses and map them to decentralized solutions. Reported threats, security mechanisms, architectural decisions, and levels of validation were grouped and reviewed critically in the patterns of limitations with respect to scalability, interoperability, and deployment maturity. Through the synthesis of fragmented results in cross disciplinary research, the paper finds the main gaps in research and comparative research results that could be used as a comprehensive reference in future studies and system design in resilient EV charging infrastructures.

Scale-of electric vehicle (EV) charging will bring great impacts to the operation of the power grid. Coordinated charging can guide these effects to the beneficial aspects. This paper firstly derived a steady mathematical model of single charge load as the basis of coordinated charging scheduling. Secondly, based on the schedulability of the EV charging load in time domain, a new kind of coordinated charging control method is put forward. An optimal model is proposed with the objective of minimum total load energy, concerning capacity of transformer and distributing lines and demands of charging time. By changing the charging time of each EV, minimum fluctuation of the total load curve be achieved. This method is verified the effectiveness by the simulation results. At last, the application design scheme in actual EV charging station is introduced for engineering application.