Secure Open Federation of IoT Platforms Through Interledger Technologies - The SOFIE Approach

As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in the U.S. using two case studies and exploring different operational strategies including sole ownership and collaborative ventures with public and private entities. The results indicate that based on the current adoption and utilization rates in the U.S., the business model involving an owner-operator collaborating with a public partner ensures profitability and protects the investment in DCFC stations from financial losses. The study also highlights that demand charges and electricity retail prices are the factors that affect the profitability of a DCFC station.

Although electric vehicle (EV) sales have been increasing over the years, worldwide EV adoption is still low. The key factors influencing this are the EV high acquisition cost and the reduced charging infrastructure. Therefore, traditional business models may not be adequate for a region and stagnate EV diffusion. Thus, designing innovative business models can be crucial to accelerate the transition to electric mobility. However, the increase in EV charging may come with drawbacks for the DSO. A possible way to mitigate those problems is to implement dynamic tariffs. Therefore, the objective of this thesis is twofold. First, to discuss the application of business models for EV adoption and charging stations (CSs) worldwide. Then, it highlights the challenges and opportunities for some business model options. Second, to assess the profitability of an e-carsharing company based on distribution local marginal price (DLMP) and vehicle-to-grid (V2G) that cooperates with the DSO through a two-stage stochastic model. The AC optimal power flow (ACOPF) is modelled using second-order cone programming (SOCP) linearized by the global polyhedral approximation. The IEEE 33 bus test system and a real Kernel distribution for the EV rental demands are used in four planning cases. The results indicate that planning disregarding the power grid perspective is the most profitable solution, but the operation may not be possible in real applications due to the high-power flows via V2G. Finally, the e-carsharing planning considering the DSO perspective increased the charging cost by 0.24% but also reduced the DLMP peak, peak demand and cable reinforcement cost by 1.5%, 5.1%, and 66%, respectively. The technical benefits brought to the DSO by the e-carsharing company could be turned into public policies and benefit both agents, increasing profit and mitigating negative impacts.

Abstract: The main requirement for the adoption and implementation of electric vehicles (EV) is the availability of charging facilities in public locations. In order to create an enabling EVSE ecosystem and hasten the adoption of EVs, this paper addresses several challenges relating to electric vehicle supply equipment (EVSE) or charging stations linked to legislation, standards, interoperability, and business models. Electric vehicle (EV) supply equipment (EVSE) or charging equipment are necessary for vehicle owners to adopt EVs. With varying degrees of success, several nations utilised various strategies and economic models to build the EVSE ecosystem. As India prepares to launch an EV revolution, a few crucial EVSE-related questions continue to plague the industry's players: What criteria apply to EVSE in India? whose will berun and keep up an EVSE? Utilities? Utility franchisees, perhaps? or outside parties like business owners, parking lot managers, and fleet operators? What will the electricity cost be for charging an EV? Will there be only energy charges or will there be capacity charges as well (minimum monthly fee per kW of capacity)? Who will cover the expense of the electric grid upgrade (higher capacity distribution transformers and new cables when necessary): the owner of the EVSE or the utility's usual grid improvement capex? Where will the public EVSEs be installed, and will the land be given away for free, at a discount, or at market value? This paper seeks to summarise the extensive work that has previously been done by numerous parties on the aforementioned topics and environment that will facilitate the rapid implementation of EVs.

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

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.

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.

While the number of the vehicle actuated with liquid fuels are settled, the count of electric vehicles is increasing. For the present moment most of them are scheduled for daily urban usage. This paper presents an analytical approach for estimation of the impact of electrical vehicle (EV) battery charging on the distribution grid. Based on the EV charge profile, load curve and local distributed generation the grid nodes, the time variation of grid parameters is obtained. A set of typical load profiles of EV charging modes is studied and presented. A software implementation and a 24h case study of low voltage distribution network with EV charging devices is presented in order to illustrate the approach and the impacts of EV charging on the grid. In the current paper an approach using variable nonlinear algebraic equations for dynamic time domain analysis of the charge of the electric vehicles is presented. Based on the results, the challenges due to EV charging in distribution networks including renewable energy sources are discussed. This approach is widely applicable for various EV charging and distributed energy resources studies considering control algorithms, grid stability analysis, smart grid power management and other power system analysis problems.

Internet of Things has emerged as a key techno-logical enabler for broader socio-technical and socio-economic paradigms, such as smart cities and Circular Economy. However, IoT systems are characterised by constraints and limitations which in order to be overcome they need to be deployed in conjunction and in synergy with other emerging ICT. Distributed Ledger Technologies (DLT) can help overcome challenges pertaining to data immutability, timeliness and security. However, the use of DLT does not satisfactorily mitigate security risks and vulnerabilities per se and currently cybersecurity aspects of IoT systems are addressed in a fragmented way. Furthermore, the conflict between the resource demanding Blockchains and the highly constrained nature of IoT devices hinders implementation efforts of corresponding systems. We consider networked systems that comprise both IoT and DLT technologies via the prism of Intelligent Transportation Systems (ITS). We elicit a three-tier threat model identifying attack vectors at the Device, the Network and the DLT layers. The identified attacks are then ranked by using the DREAD ranking scheme. The use of the threat model is demonstrated on a novel proof-of-concept IoT networked system implemented using the IOTA Tangle distributed ledger, where it helps to critically appraise the design of the system against the most critical attacks. Furthermore, the developed system is among the first in the literature to demonstrate the synergy of IoT and DLT on actual constrained embedded devices. The performance evaluation provides insights showing that such systems can be efficient and suitable for real-life deployment.

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 vehicles are seen as an option to reduce greenhouse emissions, directly related with the electricity generation mix and with the time of charging due to the variations of the generation sources during the day. At the same time, with their widespread adoption the increase in the demand for electricity to charge these vehicles could pose significant challenges to the electrical grid in terms of additional load due to unmanaged charge strategies. In order to mitigate these problems, the charging of the electrical vehicles must be managed. This paper presents the development of a system architecture to dynamically control the charging of electric vehicles to maintain the proper operation of the local distribution grid and minimize the environmental impact. The hardware consists of two modules, a meter and controllable plugs both with communication capabilities, while the software consists in a forecast and scheduler module. The forecast module calculates the load based on the power consumption behavior and uses the renewable generation forecast to assign the best time slot to charge the vehicle. The system aims to minimize the load peaks and flatten the load profile, while minimizing the environmental impacts. Based on the user preferences, system characteristics, consumption and renewable generation forecast, the system will assign the most suitable time slot to charge the electric vehicle. For the case of multiple electric vehicles, the system will schedule their charge based on a calculated priority level, in order to maintain a reliable operation of the local electrical grid.

The integration of electric vehicle (EV) charging and Photovoltaic (PV) systems at residential buildings has increased in recent years and poses new challenges for the power system. Smart charging of EVs is believed to be one of the solutions to problems arising from PV and EV integration since it can improve the synergy between PV generation and EV charging. Accurate forecasts of PV generation plays an important role in smart charging schemes to optimally utilize the PV electricity for EV charging. This paper presents an assessment of a direct forecasting method applied to an EV smart charging scheme. Direct forecasting is a forecasting method which focus directly on the link between the forecast origin and the targeted horizon. The objective of the smart charging in this study is to minimize the net-load variability, which will also increase the self-consumption of PV electricity and reduce the peak loads. The PV self-consumption ratios in different forecast scenarios are compared. Results show that the smart charging with the direct forecast can achieve up to 89% of the PV self-consumption performance of the scheme with perfect forecast.

<div class="section abstract"><div class="htmlview paragraph">Conventional charging systems for electric and plug-in hybrid vehicles currently use cables to connect to the grid. This methodology creates several disadvantages, including tampering, risk, depreciation and non-value added user efforts. Loose or faulty cables may also create a safety issue. Wireless charging for electric vehicles delivers both a simple, reliable and safe charging process. The system enhances consumer adoption and promotes the integration of electric vehicles into the automotive market. Increased access to the grid enables a higher level of flexibility for storage management, increasing battery longevity.</div><div class="htmlview paragraph">The power class of 3.7kW or less is an optimal choice for global standardization and implementation, due to the readily available power installations for potential customers throughout the world. One of the key features for wireless battery chargers are the inexpensive system costs, reduced content and light weight, easing vehicle integration.</div><div class="htmlview paragraph">This paper demonstrates a wireless charging design with minimal component content. It includes a car pickup coil with 300 mm side length and low volume and mass 1.5 dm<sup>3</sup> power interface electronics. After an overview of its hardware requirements, power transfer and efficiency benefits are presented, providing the anticipated horizontal and lateral deviations.</div><div class="htmlview paragraph">An intense magnetic field is required to transfer the target power at low volumes between the transfer units. This field heats up any metal object over the transfer coil, similar to an induction oven. Consequently, the system should be powered down whenever a metal object is detected in this area. A Foreign Object Detection (FOD) design has been developed to continuously monitor the critical high field area. Device testing results are also provided.</div><div class="htmlview paragraph">Field characteristics are verified alongside the vehicle, ensuring system safety for living beings; compliant with all applicable standards reference limits which is more challenging than the basic limits [<span class="xref">13</span>].</div></div>

Routing and charging of electric vehicles: Literature review

Uncontrolled charging of electric vehicles can affect the power system operation and stability. In cities with high population density and with short driving distances like Singapore, car parks bear a high proportion of private vehicles most of the day. These car parks can be regarded as aggregators and control the charging process. Demand response could be used by the car park operators as an additional source of revenue and thus help offset the high installation costs. This paper investigates different charging strategies and demand response management possibilities within a specific car park. Data for arrival times and parking durations are obtained using available statistical information, charging of individual electric vehicles is controlled by the car park operator and effects of demand response management on the total charging cost of the system is analysed.

Distributed Ledger Technologies (DLTs), including but not limited to Blockchain, have become key enablers of digital transformation across sectors. Their core attributes — immutability, traceability, decentralization, and tamper resistance — make them well-suited for secure and transparent data management. While initially linked to cryptocurrencies, DLTs are now applied in diverse domains such as identity verification, supply chain management, secure voting, and digital asset certification.In parallel, rapid advances in artificial intelligence (AI), particularly in Generative AI, are reshaping how data is generated, interpreted and applied. AI enables new forms of automation and decision-making, but it also introduces risks related to misinformation, digital fraud and security vulnerabilities. The convergence of DLT and AI offers a promising framework to address these challenges. As decentralized trust infrastructures, DLTs can enhance the transparency and integrity of AI systems by validating identities, certifying data provenance and securing sensitive assets such as contracts, medical records and intellectual property.This integration fits within the broader BIBI paradigm – Blockchain, AI, Big Data and the Internet of Things (IoT), where DLTs provide the foundation for secure, distributed coordination and auditability. Coupled with AI, they enable automated decision-making over large-scale, heterogeneous data, particularly from IoT devices, with applications in manufacturing, logistics and health care. This synergy also unlocks advanced functionalities, including decentralized identity, verifiable credentials and trusted automation, facilitating use cases such as tokenized assets and secure digital certification.Nevertheless, several technical and ethical challenges remain. The increasing computational demands of AI raise concerns about energy consumption, an issue already encountered in early DLT deployments. Energy-efficient consensus mechanisms, such as Proof-of-Stake and Byzantine Fault Tolerance, offer valuable insights for developing more sustainable AI infrastructures. Another key challenge is interoperability, as most DLT platforms operate in isolation. Enhancing interoperability would enable seamless data and asset exchange across heterogeneous ledgers, contributing to a more integrated and user-centric digital ecosystem.Looking forward, the convergence of DLT and AI holds significant potential for intelligent smart contracts, advanced traceability in critical sectors such as food safety and health care, and the development of digital twins for real-time simulation and optimization of organizational processes. This cross-disciplinary innovation can drive not only technological and economic growth but also democratic access to trusted, privacy-preserving, and sustainable digital systems.The Special Issue “Distributed Ledger Technologies and Artificial Intelligence” aims to offer a dedicated forum for researchers exploring the intersection of these two rapidly evolving fields. A total of 13 submissions were received, and following a rigorous double-blind peer review process, with at least three reviews per manuscript, three articles were selected for publication.The first article, “Energy: Reducing Latency in IoT DLTs for AI-Driven Real-Time Solutions” (Moya Perez et al., 2025), addresses the challenges of integrating IoT networks with DLT and AI, focusing on latency and energy efficiency. The authors propose Energy, a novel consensus algorithm designed for public DAG-based DLTs, which reduces or eliminates Proof-of-Work requirements. Experimental results demonstrate substantial improvements in low-payload scenarios common in IoT, enabling real-time AI model updates and efficient, secure data flow in large-scale environments.The second paper, “eFLEET: A Framework in Federated Learning for Enhanced Electric Transportation” (Ruiz de Gauna et al., 2025), presents a federated learning approach for optimizing autonomous electric vehicle routing in urban settings. Using traffic image analysis through computer vision and a DAG-based privacy layer, the framework selects optimal routes based on real-time and predictive data. The experiments show how the system improves traffic planning, enhances scalability and contributes to energy efficiency and pollution reduction in smart cities.The third contribution, “Enhancing the Viewing, Browsing and Searching of Knowledge Graphs with Virtual Properties” (Dibowski, 2024), introduces virtual properties as a user-interface enhancement for knowledge graph exploration. Defined using SHACL and evaluated via SPARQL queries, virtual properties enrich class scopes beyond direct neighbors. Successfully implemented at Bosch for over 100,000 users, this novel approach improves usability and expressiveness in knowledge-driven applications and sets a new direction for SHACL-based UI development.The authors would like to express their sincere gratitude to all the authors who submitted their contributions and to the anonymous reviewers for their thoughtful and rigorous evaluations. The authors also extend their appreciation to Professor Honghao Gao, Editor-in-Chief of IJWIS (Emerald), for his support in the publication of this special issue. The authors believe the selected papers represent cutting-edge research in this area and will be of great interest to the DLT and AI research communities.