Search Results (1,172)

This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems incorporate photovoltaic (PV) systems, lithium-ion battery energy storage systems (ESS), and diesel generators. A comprehensive analysis was conducted in Cairo, Egypt, focusing on small vehicle charging needs in both grid-connected and generator-supported scenarios. In this study, a 468 kW PV array integrated with 29 units of 1 kWh lithium-ion batteries and supported by time-of-use (TOU) tariffs, were used to optimize energy utilization. This study demonstrated the feasibility of the system in a case of eight chargers of 150 kW each and forty chargers of 48 kW. Conclusions suggest that the PV + ESS has the lowest pure power costs and reduced carbon emissions compared to traditional network-dependent solutions. The optimal configuration of USD 10.23 million over 25 years, with lifelong savings, results in annual savings of tool billing of around USD 409,326. This study concludes that a solar-powered EVC in Egypt is both technically and economically attractive, especially in the light of increasing energy costs. Full article

The growing deployment of electric vehicles (EVs) offers a unique opportunity to utilize them as mobile energy resources during large-scale emergencies. However, existing emergency dispatch strategies often neglect the compounded uncertainties of hazard disruptions, infrastructure fragility, and user behavior. To address this gap, we propose the Emergency-Responsive Aggregation Framework (ERAF)—a behaviorally informed, spatially aware, and probabilistic optimization model for resilient EV energy dispatch. ERAF integrates a Bayesian inference engine to estimate plug-in availability based on hazard exposure, behavioral willingness, and charger operability. This is dynamically coupled with a GIS-based spatial filter that captures road inaccessibility and corridor degradation in real time. The resulting probabilistic availability is fed into a multi-objective dispatch optimizer that jointly considers power support, response time, and delivery reliability. We validate ERAF using a high-resolution case study in Southern California, simulating 122,487 EVs and 937 charging stations across three compound hazard scenarios: earthquake, wildfire, and cyberattack. The results show that conventional deterministic models overestimate dispatchable energy by up to 35%, while ERAF improves deployment reliability by over 28% and reduces average delays by 42%. Behavioral priors reveal significant willingness variation across regions, with up to 47% overestimation in isolated zones. These findings underscore the importance of integrating behavioral uncertainty and spatial fragility into emergency energy planning. ERAF demonstrates that EVs can serve not only as grid assets but also as intelligent mobile agents for adaptive, decentralized resilience. Full article

Mass market adoption of EVs is critical for decreasing greenhouse gas emissions and dependence on fossil fuels. However, this transition faces significant challenges, particularly the limited availability of public charging infrastructure. Expanding charging stations and renewable integrated EV parking lots can accelerate the adoption of EVs by enhancing charging accessibility and sustainability. This paper introduces an integrated optimization framework to determine the optimal siting of a Residential Parking Lot (RPL), a Commercial Parking Lot (CPL), and an Industrial Fast Charging Station (IFCS) within the IEEE 33-bus distribution system. In addition, the optimal sizing of rooftop solar photovoltaic (SPV) systems on the RPL and CPL is addressed to enhance energy sustainability and reduce grid dependency. The framework aims to minimize overall power losses while considering long-term technical, economic, and environmental impacts. To solve the formulated multi-dimensional optimization problem, Horse Herd Optimization (HHO) is used. Comparative analyses on IEEE-33 bus demonstrate that HHO outperforms well-known optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) in achieving lower energy losses. Case studies show that installing a 400-kW rooftop PV system can reduce daily energy expenditures by up to 51.60%, while coordinated vehicle scheduling further decreases energy purchasing costs by 4.68%. The results underscore the significant technical, economic, and environmental benefits of optimally integrating EV charging infrastructure with renewable energy systems, contributing to more sustainable and resilient urban energy networks. Full article

This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

The use of the Industrial Internet of Things within the domain of electric vehicles signifies a paradigm shift toward advanced, integrated, and optimized transport systems. This study thoroughly investigates the pivotal role of the Industrial Internet of Things in elevating various features of electric vehicle technology, comprising predictive maintenance, vehicle connectivity, personalized user management, energy and fleet optimization, and independent functionalities. Key IIoT applications, such as Vehicle-to-Grid integration and advanced driver-assistance systems, are examined alongside case studies highlighting real-world implementations. The findings demonstrate that IIoT-enabled advanced charging stations lower charging time, while grid stabilization lowers electricity demand, boosting functional sustainability. Battery Management Systems (BMSs) prolong battery lifespan and minimize maintenance intervals. The integration of the IIoT with artificial intelligence (AI) optimizes route planning, driving behavior, and energy consumption, resulting in safer and more efficient autonomous EV operations. Various issues, such as cybersecurity, connectivity, and integration with outdated systems, are also tackled in this study, while emerging trends powered by artificial intelligence, machine learning, and emerging IIoT technologies are also deliberated. This study emphasizes the capacity for IIoT to speed up the worldwide shift to eco-friendly and smart transportation solutions by evaluating the overlap of IIoT and EVs. Full article

Three-level non-inverting buck–boost converters are promising for electric vehicle charging stations due to their wide voltage regulation capability and bidirectional power flow. However, the number of three-level operating states is four times that of two-level operating states, and the lack of a unified switching state selection mechanism leads to serious challenges in its application. To address these issues, a finite control set model predictive control (FCS-MPC) strategy is proposed, which can determine the optimal set and select the best switching state from the excessive number of states. Not only does the proposed method achieve fast regulation over a wide voltage range, but it also maintains the input- and output-side capacitor voltage balance simultaneously. A further key advantage is that the number of switching actions in adjacent cycles is minimized. Finally, a hardware-in-the-loop experimental platform is built, and the proposed control method can realize smooth transitions between multiple operation modes without the need for detecting modes. In addition, the state polling range and the number of switching actions are superior to conventional predictive control, which provides an effective solution for high-performance multilevel converter control in energy systems. Full article

Electric vehicles (EVs) have gained significant attention in recent decades for their environmental benefits. However, their widespread adoption poses challenges due to limited charging infrastructure and long charging times, often resulting in underutilized charging stations (CSs) and unnecessary queues that complicate travel planning. Therefore, selecting the appropriate CS is essential for minimizing the total travel time of EVs, as it depends on both driving time and the required charging duration. This selection process requires estimating the energy required to reach each candidate CS and then continue to the destination, while also checking if the EV’s battery level is sufficient for a direct trip. To address this gap, we propose an integrated platform that leverages two ensemble machine learning models: Bi-LSTM + XGBoost to predict energy consumption, and FFNN + XGBoost for identifying the most suitable CS by considering required energy, waiting time at CS, charging speed, and driving time based on varying traffic conditions. This integration forms the core novelty of our system to optimize CS selection to minimize the total trip duration. This approach was validated with SUMO simulations and OpenStreetMap data, demonstrating a mean absolute error (MAE) ranging from 2.29 to 4.5 min, depending on traffic conditions, outperforming conventional approaches that rely on SUMO functions and mathematical calculations, which typically yielded MAEs between 5.1 and 10 min. These findings highlight the proposed system’s effectiveness in reducing total travel time, improving charging infrastructure utilization, and enhancing the overall experience for EV drivers. Full article

Electric vehicle (EV) charging infrastructures raise significant concerns about data security and user privacy because traditional centralized authorization and billing frameworks expose sensitive information to breaches and profiling. To address these vulnerabilities, we propose a novel decentralized framework that couples a permissioned blockchain with fully homomorphic encryption (FHE). Unlike prior blockchain-only or blockchain-and-machine-learning solutions, our architecture performs all authorization and billing computations on encrypted data and records transactions immutably via smart contracts. We implemented the system on Hyperledger Fabric using the CKKS-based TenSEAL library, chosen for its efficient arithmetic on real-valued vectors, and show that homomorphic operations are executed off-chain within a secure computation layer while smart contracts handle only encrypted records. In a simulation involving 20 charging stations and up to 100 concurrent users, the proposed system achieved an average authorization latency of 610 ms, a billing computation latency of 310 ms, and transaction throughput of 102 Tx min while maintaining energy overhead below 0.14 kWh day per station. When compared to state-of-the-art blockchain-only approaches, our method reduces data exposure by 100%, increases privacy from “moderate” to “very high,” and achieves similar throughput with acceptable computational overhead. These results demonstrate that privacy-preserving EV charging is practical using present-day cryptography, paving the way for secure, scalable EV charging and billing services. Full article

Electric vehicle (EV) charging stations play a pivotal role in the widespread adoption and integration of electric vehicles into mainstream transportation systems. While the effects of climate change and greenhouse gases are increasing worldwide, the transition to electric vehicles is of high importance in terms of both ecological and sustainability. EV charging stations serve as the backbone of this transition, providing essential infrastructure to support the charging needs of EV owners and facilitate the transition to electric vehicles. In this study, a MINLP mathematical model is developed for the multi-objective optimization of EVCS. For implementation, Istanbul’s European side and a large-scale synthetic case are addressed considering both current demand and estimations for low, medium, and high EV numbers by the Energy Market Regulatory Authority (EMRA) for 2030 and 2035. The primary aim is to minimize station numbers, capacity, waiting time, and station idle time while meeting the demand. During the solvation of the mathematical model, both present demand and future EV usage forecasts are taken into consideration. This involves simulating different scenarios using EMRA’s 2030 and 2035 estimates and determining the optimal locations and capacities for charging stations for each demand level. Efficiencies in different scenarios were evaluated and the created mathematical model provides to optimize EV charging stations in multiple ways, there will be savings in total cost and labor force. The findings of the study will provide a valuable guide to the EV charging station infrastructure planning of the highways, regions, and urban areas to be selected in possible studies. The multi-directional optimization model addressed in this study will support decision-makers and industry experts in making informed decisions towards the sustainable and efficient development of EV charging infrastructure. Full article

The number of electric vehicles is constantly increasing in Europe and around the world. Providing a reliable charging infrastructure for the se vehicles is a major challenge for distribution grid operators. Off-grid microgrids have become a promising solution to this challenge, using renewable energy sources such as solar power to meet the demand in a sustainable way. This paper presents a practical study of a solar-powered microgrid operating at a university campus in Ilmenau, Germany, aimed at supporting electric vehicle (EV) charging at public workplaces. The system includes eight charging stations and utilizes renewable energy to reduce grid dependency. Statistical methods, including distribution functions, medians, and mean values, were applied to classify and evaluate the dataset to analyze energy generation and variable load patterns, as well as system performance. The results show that the Ilmenau microgrid can meet EV charging demand during the warm season but underperform during the cold season. An economic analysis determined costs of EUR 0.58/kWh based on pre-2020 component prices and EUR 0.46/kWh based on 2025 market prices. The calculated annual cost per employee is EUR 308.29 over a 20-year period. Increasing energy storage was found to be neither cost-effective nor operationally beneficial. The scalability of the microgrid to larger workplaces is investigated, and recommendations for system improvements are provided. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

This article examines the evolution of the most extensively researched subjects in e-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in terms of e-mobility, can learn from in its efforts to adopt electric vehicles (EVs). To strengthen the body of knowledge and determine the most effective and efficient route to an “EV-ready” nation, the authors compiled data on the latest developments in the EV industry. A bibliometric analysis was performed on 3962 articles using VOSviewer software, which identified six noteworthy clusters that warranted further discussion. Additionally, we examined the sequential progression of these clusters as follows: (1) the environmental ramifications of electric mobility; (2) advancements in EV technology, including range extension and soundless engines, as well as the capital expenditure (CAPEX) and operating expenditure (OPEX) of purchasing and operating EVs; (3) concerns regarding the effectiveness and durability of EV batteries; (4) the availability of EV charging stations and grid integration; (5) charging time; and, finally, (6) the origin and source of the energy used in the development of e-mobility. Delineating critical aspects in the development of e-mobility can help to equip policymakers and decision makers in Kuwait in formulating timely and economical choices pertaining to sustainable transportation. This study contributes by cross-walking six global bibliometric clusters to Kuwait’s ten EV adoption barriers and mapping each to actionable policy levers, linking evidence to deployment guidance for an emerging market grid. Unlike prior bibliometric overviews, our analysis is Kuwait-specific and heat-contextual, and it reports each cluster’s size and recency to show where the field is moving. Using Kuwait driving logs, we found that summer (avg 43.2 °C) reduced the effective full-charge range by 24% versus pre-winter (approximately 244 km vs. 321 km), underscoring the need for shaded PV-coupled hyper-hubs and active thermal management. Full article

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however the appearance of automatic charging stations with higher Technology Readiness Levels (TRL) can only be dated from 2019 onwards. In most of the developed concepts and solutions, a dedicated parking system is required by vehicle drivers, since the operating range of the robots used for charging is limited. In most cases, solutions do not incorporate robots with unique geometries; instead, proven industrial solutions are applied. The robots in these prototypes are typically installed in a fixed position, similar to industrial applications, and are not mobile. The charging of one vehicle is usually performed by one robot. A high-level summary of the developed mechanical system is presented in this project overview. In this research, an automated, robot-controlled electric vehicle charging system was designed, in which vehicles are parked perpendicularly adjacent to each other, and multiple vehicles are charged using a single collaborative robot. The mechanical system was implemented with a robot mounted on an extendable arm attached to a carriage, which is guided in two directions along rails. In this manner, the automatic charging system is positioned precisely at the parking location of the vehicle to be charged. Full article

Implementing an effective energy management system (EMS) is essential for optimizing electric vehicle (EV) charging stations (EVCSs), especially when combined with battery energy storage systems (BESSs). This study analyzes a real-world EVCS scenario and compares several EMS approaches, aiming to reduce operating costs while accounting for BESS degradation. Initially, significant savings were achieved by optimizing the EV charging schedule using genetic algorithms (GAs), even without storage. Next, different BESS-based EMSs, including rule-based and fuzzy logic systems, were optimized via GAs. Finally, in a dynamic scenario with variable electricity prices and demand, the adaptive GA-optimized fuzzy logic EMS was found to achieve the best performance, reducing annual operating costs by 15.6% compared to the baseline strategy derived from real fleet data. Full article