Search Results (697)

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions. Full article

With the impact of fossil energy on the climate environment and the development of energy technologies, new energy vehicles, represented by electric cars, have begun to receive increasing attention and emphasis. The rapid proliferation of public charging infrastructure for NEVs has concurrently influenced traditional petrol station networks, creating measurable disparities in their spatial distributions that warrant systematic investigation. This research examines Wenzhou City, China, as a representative case area, employing multi-source Point of Interest (POI) data and spatial analysis models to analyse differential characteristics in spatial layout accessibility, service equity, and underlying driving mechanisms between public electric vehicle charging stations (EV) and traditional gas stations (GS). The findings reveal that public electric vehicle charging stations exhibit a pronounced “single-centre concentration with weak multi-centre linkage” spatial configuration, heavily reliant on dual-core drivers of population density and economic activity. This results in marked service accessibility declines in peripheral areas, resembling a cliff-like drop, and a relatively low spatial equity index. In contrast, traditional gas stations demonstrate a “core-axis linkage” diffusion pattern with strong coupling to urban road networks, showing gradient attenuation in service coverage efficiency along transportation arteries, fewer suburban service gaps, and more gradual accessibility reductions. Location entropy analysis further indicates that charging station deployment shows significant capital-oriented tendencies, with certain areas exhibiting paradoxical “excess facilities” phenomena, while gas station distribution aligns more closely with road network topology and transportation demand dynamics. Furthermore, the layout characteristics of public charging stations feature a more complex and diverse range of land use types, while traditional gas stations have a strong dependence on industrial land. This research elucidates the spatial distribution patterns of emerging and legacy energy infrastructure in the survey regions, providing critical empirical evidence for optimising energy infrastructure allocation and facilitating coordinated transportation system transitions. The findings also offer practical insights for the construction of energy supply facilities in urban development frameworks, holding substantial reference value for achieving sustainable urban spatial governance. Full article

The rapid growth of electric vehicles (EVs) has significantly increased the demand for charging infrastructure, posing a challenge in balancing charging demand and infrastructure supply. The development of battery swapping and charging stations (BSCSs) is crucial for addressing these challenges and serves as a fundamental pillar for the sustainable advancement of EVs. This study develops a bi-level optimization model for the location and route planning of BSCSs. The upper-level model optimizes station locations to minimize total cost and service delay, while the lower-level model optimizes driver travel routes to minimize total time. An updated Non-Dominated Sorting Genetic Algorithm (UNSGA) is applied to enhance solution efficiency. The experimental results show that the bi-level model outperforms the single-level model, reducing total cost by 1.5% and travel time by 6.6%. Compared to other algorithms, the UNSGA achieves 9.43% and 8.23% lower costs than MOPSO and MOSA, respectively. Furthermore, BSCSs, despite 15.42% higher construction costs, reduce driver travel time by 22.43% and waiting time by 71.19%, highlighting their operational advantages. The bi-level optimization method provides more cost-effective decision support for EV infrastructure investors, enabling them to adapt to dynamic drivers’ needs and optimize resource allocation. Full article

Solid-state transformer (SST)-based ultra-fast charging stations (UFCSs) are emerging as a key technology in next-generation electric vehicle (EV) infrastructure, addressing the growing demand for efficient charging. Unlike traditional line-frequency transformers, SSTs offer higher energy conversion efficiency, smaller size, and better scalability. However, challenges such as control complexity and grid stability remain. This review analyzes existing SST topologies (both classical and new) and control methods, discussing their impact on system performance, and finally, it provides an outlook on future technological trends. Full article

This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors. Full article

As electric vehicle (EV) adoption accelerates, residential buildings—particularly multi-dwelling structures—face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings. Full article

With the rapid rise in electric vehicle (EV) adoption, the deployment of EV charging infrastructure—particularly fast-charging stations—has expanded significantly to meet growing energy demands. While fast charging offers the advantage of reduced charging time and improved user convenience, it imposes considerable stress on existing power distribution systems due to its high power and current requirements. This study investigated the impact of EV fast charging on power quality within Thailand’s distribution network, emphasizing compliance with accepted standards such as IEEE Std 519-2014. We developed a control-oriented EV-charging station model in power systems computer-aided design and electromagnetic transients, including DC (PSCAD/EMTDC), which integrates grid-side vector control with DC fast-charging (CC/CV) behavior. Active/reactive power setpoints were mapped onto $dq$ current references via Park’s transformation and regulated by proportional integral (PI) controllers with sinusoidal pulse-width modulation (SPWM) to command the voltage source converter (VSC) switches. The model enabled dynamic studies across battery state-of-charge and staggered charging schedules while monitoring voltage, current, and total harmonic distortion (THD) at both transformer sides, charger AC terminals, and DC adapters. Across all scenarios, the developed control achieved grid-current THDi of <5% and voltage THD of <1.5%, thereby meeting IEEE 519-2014 limits. These quantitative results show that the proposed, implementation-ready approach maintains acceptable power quality under diverse fast-charging patterns and provides actionable guidance for planning and scaling EV fast-charging infrastructure in Thailand’s urban networks. Full article

The burgeoning demand for reliable fault detection in high-voltage power equipment necessitates advanced sensing materials capable of identifying trace sulfur hexafluoride SF₆ decomposition products (SDPs). In this work, the first-principles calculations were employed to comprehensively evaluate the potential of Ir- and Pt-doped InTe (Ir-InTe and Pt-InTe) monolayers as high-performance gas sensors for the four specific SDPs (H₂S, SO₂, SOF₂, SO₂F₂). The results reveal that Ir and Pt atoms are stably incorporated into the hollow sites of the InTe monolayer, significantly reducing the intrinsic bandgap from 1.536 eV to 0.278 eV (Ir-InTe) and 0.593 eV (Pt-InTe), thereby enhancing the material’s conductivity. Furthermore, Ir-InTe exhibits selective chemisorption for H₂S, SO₂, and SOF₂, with adsorption energies exceeding −1.35 eV, while Pt-InTe shows chemisorption capability for all four SDPs. These interactions are further supported by significant charge transfer and orbital hybridization. Crucially, these interactions induce notable bandgap changes, with Ir-InTe showing up to a 65.5% increase (for SOF₂) and Pt-InTe showing an exceptional 105.2% increase (for SO₂F₂), alongside notable work function variations. Furthermore, recovery time analysis indicates that Ir-InTe is suitable for reusable H₂S sensing at 598 K (0.24 s), whereas Pt-InTe offers recyclable detection of SO₂ (5.27 s) and SOF₂ (0.16 s) at the same temperature. This work provides theoretical guidance for the development of next-generation InTe-based gas sensors for the fault diagnosis in high-voltage power equipment. Full article

Alzheimer’s disease (AD) has emerged as a global health threat that demands early detection to seize the optimal intervention opportunity. Central nervous system (CNS)-derived extracellular vesicles (EVs), lipid-bilayer nanoparticles released by CNS cells, carry key biomolecules involved in AD pathology, positioning them as a promising source of biomarkers for early detection. Current breakthroughs in EV-based isolation and detection technologies have opened up the possibility of early, accurate AD diagnosis. This review summarizes their multifaceted roles in AD pathogenesis, including amyloid-β (Aβ) aggregation, tau propagation, neuroinflammation, and synaptic dysfunction, and highlights neuron- and glia-derived EV biomarkers with translational potential. We further outline recent advances in EV isolation techniques—including density-, size-, charge/dielectric-, immunoaffinity-, and acoustics-based approaches—and emerging detection platforms such as fluorescence, surface plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), electrochemical, and nanomechanical sensors for sensitive, multiplex AD diagnostics. Finally, we discuss key challenges, including standardization, sensitivity, and high-throughput adaptation, and explore future directions such as automated microfluidics and single-vesicle analysis. CNS-derived EVs hold significant promise as minimally invasive, next-generation tools for early AD detection and precision medicine. 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

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

This paper aims to explore Virtual Power Plants (VPPs) in combination with Demand Response (DR) concepts, integrating solar power generation, Electric Vehicle (EV) charging and discharging, and user loads to establish an optimal energy management scheduling system. Willingness curves for load curtailment are derived based on the consumption patterns of industrial, commercial, and residential users, enabling VPPs to design DR mechanisms under Time-of-Use (TOU), two-stage, and critical peak pricing periods. An energy management model for a VPP is developed by integrating DR, EV charging and discharging, and user loads. To solve this model and optimize economic benefits, this paper proposes an Improved Wolf Pack Search Algorithm (IWPSA). Based on the original Wolf Pack Search Algorithm (WPSA), the Improved Wolf Pack Search Algorithm (IWPSA) enhances the key behaviors of detection and encirclement. By reinforcing the attack strategy, the algorithm achieves better search performance and improved stability. IWPSA provides a parameter optimization mechanism with global search capability, enhancing searching efficiency and increasing the likelihood of finding optimal solutions. It is used to simulate and analyze the maximum profit of the VPP under various scenarios, such as different seasons, incentive prices, and DR periods. The verification analysis in this paper demonstrates that the proposed method can not only assist decision makers in improving the operation and scheduling of VPPs, but also serve as a valuable reference for system architecture planning and more effectively evaluating the performance of VPP operation management. Full article

As electric vehicles (EVs) gain widespread adoption in industrial environments supported by Industrial Internet of Things (IIoT) smart grids technology, coordinated charging of multiple EVs has become vital for maintaining grid stability. In response to the scalability challenges faced by traditional algorithms in multi-device environments and the limitations of discrete action spaces in continuous control scenarios, this paper proposes a dynamic charging scheduling algorithm for EVs based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The algorithm combines real-time electricity prices, battery status monitoring, and distributed sensor data to dynamically optimize charging and discharging strategies of multiple EVs in continuous action spaces. The goal is to reduce charging costs and balance grid load through coordinated multi-agent learning. Experimental results show that, compared with baseline methods, the proposed MADDPG algorithm achieves a 41.12% cost reduction over a 30-day evaluation period. Additionally, it effectively adapts to price fluctuations and user demand changes through Vehicle-to-Grid technology, optimizing charging time allocation and enhancing grid stability. Full article

The fast-changing scenario of the transportation industry due to the rapid adoption of electric vehicles (EVs) imposes significant challenges on power distribution networks. Challenges such as dynamic and concentrated charging loads necessitate intelligent demand-side management (DSM) strategies to ensure grid stability and cost efficiency. This study proposes a novel two-stage framework integrating Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and linear programming (LP) to optimize EV charging loads across four operational scenarios: Summer Weekday, Summer Weekend, Winter Weekday, and Winter Weekend. Utilizing a dataset of 72,856 real-world charging sessions, the first stage employs HDBSCAN to segment charging behaviors into nine distinct clusters (Davies-Bouldin score: 0.355, noise fraction: 1.62%), capturing temporal, seasonal, and behavioral variability. The second stage applies linear programming optimization to redistribute loads under real-time pricing (RTP), minimizing operational costs and peak demand while adhering to grid constraints. Results demonstrate the load optimization by total peak reductions of 321.87–555.15 kWh (23.10–25.41%) and cost savings of $27.35–$50.71 (2.87–5.31%), with load factors improving by 14.29–17.14%. The framework’s scalability and adaptability make it a robust solution for smart grid integration, offering precise load management and economic benefits. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article