Search Results (11,114)

Wireless power transfer (WPT) has emerged as a key enabling technology for the large-scale adoption of electric vehicles (EVs), offering enhanced charging flexibility, improved safety, and seamless integration with intelligent transportation and renewable energy infrastructures. This paper presents a comprehensive review and technical synthesis of WPT technologies spanning both near-field and far-field domains, including inductive power transfer (IPT), magnetically coupled resonant WPT (MCR-WPT), capacitive power transfer (CPT), microwave power transfer (MPT), and laser wireless charging (LPT). Particular emphasis is placed on MCR-WPT, the most widely adopted approach for EV wireless charging, for which the coupler structures, resonant compensation networks, power converter architectures, and control strategies are systematically analyzed. The review further identifies that hybrid WPT architectures, adaptive compensation design and wide-coverage coupling mechanisms will be central to enabling high-power, long-distance, and misalignment-resilient wireless charging solutions for next-generation electric transportation systems. Full article

Energetic powder processing includes comminution, sieving, drying, conveying, mixing, and packaging, all of which determine product performance and safety. With growing requirements for efficiency and reliability, numerical simulation has become essential for analyzing mechanisms, optimizing parameters, and supporting equipment design. This review summarizes recent progress in simulation techniques such as the discrete element method (DEM), computational fluid dynamics (CFD), and multi-scale coupling while also evaluating their predictive capabilities and limitations across various unit operations and safety concerns such as electrostatic hazards. It, thus, establishes the core “property–parameter–performance” relationships and clarifies mechanisms in multiphase flow, energy transfer, and charge accumulation, and highlights the role of symmetry in improving simulation efficiency. By highlighting persistent challenges, this work lays a foundation for future research, guiding the development of theoretical frameworks and practical solutions for advanced powder processing. Full article

The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more well-known and are being quickly used worldwide. However, the exponential rise in EV sales has also raised a number of issues, which are becoming important and demanding. These challenges include the need of driving security, the battery degradation, the inadequate infrastructure for charging EVs, and the uneven energy distribution. In order for EVs to reach their full potential, intelligent systems and innovative technologies need to be introduced in the field of EVs. This is where business intelligence (BI) can be employed, along with artificial intelligence (AI), data analytics, and machine learning. In this paper, we provide a comprehensive survey on the use of BI strategies in the EV transportation sector. We first introduce the EVs and charging station technologies. Then, research works on the application of BI and data analysis techniques in EV technology are reviewed to further understand the challenges and open issues for the research and industry community. Moreover, related works on accident analysis, battery health prediction, charging station analysis, intelligent infrastructure, locating charging stations analysis, and autonomous driving are investigated. This survey systematically reviews 75 peer-reviewed studies published between 2020 and 2025. Finally, we discuss the fundamental limitations and the future open challenges in the aforementioned topics. Full article

Energy-efficient driving is essential for reducing the environmental impacts of road transport, especially for electric passenger vehicles. This research aims to build a data-driven behavioral analysis and energy-consumption evaluation model. The model relies on sensor data from the vehicle’s on-board communication network, primarily the CAN (Controller Area Network) bus. We analyze patterns of key powertrain and battery parameters—such as current, voltage, state of charge (SoC), and power—in relation to driver inputs, such as the accelerator pedal position. In the first stage, we review the literature with a focus on machine learning and clustering methods used in behavioral and energy analysis. We also examine the role of on-board telemetry systems. Next, we develop a controlled measurement architecture. It defines reference consumption maps from dynamometer data across operating points and environmental variables, including SoC, temperature, and load. The longer-term goal is a multidimensional behavioral map and profiling framework that can predict energy efficiency from real-time driver inputs. This work lays the foundation for a future system with adaptive, feedback-based driver support. Such a system can promote intelligent, sustainable, and behavior-oriented mobility solutions. Full article

This study presents the electrification plan of a school bus (SB) fleet and examines its potential in vehicle-to-grid (V2G) applications. The data collected includes the efficiency of a 120 kW EV charger, energy consumption of a 40-foot electric school bus (ESB), and a diesel bus operating on the same route. The energy consumption data of the ESB and diesel school bus (DSB) were processed to derive the yearly average distance-specific energy consumption of 0.37 mile/kWh (0.60 km/kWh) grid electricity and 5.55 MPG (2.36 km/L), respectively. The energy consumption ratio of the ESB over the DSB is 14.92 kWh/gallon (3.94 kWh/L) diesel. Based on the CO₂ intensity, 1.956 lb/kWh (0.887 kg/kWh) of electricity produced in WV and that of diesel fuel, the distance-specific CO₂ emissions of the ESB were 5.38 lb/mile (1.52 kg/km), which are higher than the 4.08 lb/mile (1.15 kg/km) from the diesel bus operating on the same route. This study also presents the V2G potential of the proposed electrical school bus fleet. Based on the estimated grid-to-vehicle battery (G2VB) efficiency of 92% and vehicle battery-to-grid (VB2G) efficiency of 92%, the grid–vehicle battery–grid (G2VB2G) efficiency is 84.64%. The application of V2G technology is associated with a loss of electricity. Based on the 20% to 80% battery charge, and the estimated 92% VB2G efficiency, the proposed ESB fleet has the potential to provide 14,929 kWh electricity, 55.2% of the ESB fleet battery capacity. The increased cost associated with the implementation of the proposed V2G is about USD 7.5 million, a 400% increase compared to the charger satisfying the operation of ESBs when V2G is not used. The V2G application also is expected to increase the charging cycles, which raises concerns about battery degradation and its replacement during SB service lifetime. Accordingly, more research work is needed to address the increased cost and grid capacity demand, and battery degradation associated with V2G applications. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

As the core technology of the new energy revolution, lithium-ion batteries have broad development prospects and significant strategic importance. With continuous improvements in energy density, enhanced safety, and breakthroughs in fast-charging technology, lithium-ion batteries will play a more substantial role in fields such as new energy vehicles and energy storage. Nevertheless, the development of the lithium-ion battery industry still faces safety issues related to thermal runaway risks. The intense exothermic reactions during thermal runaway can release flammable gases, potentially leading to uncontrolled combustion or explosions, thereby posing major safety threats. This paper reviews the analysis of gas composition and patterns during lithium-ion battery thermal runaway under different conditions, as well as research on gas explosion characteristics. It introduces advanced methods for gas detection and suppression during thermal runaway and summarizes studies on the chemical kinetic mechanisms and predictive models of gas generation during thermal runaway. These studies provide a scientific basis for improving the reliability of renewable energy storage systems and formulating and refining battery safety standards. Full article

Three derivatives of benzo-[f]-quinolinium ylids, which all underwent an intermolecular charge transfer process, were used as solvatochromic indicators to study the specific solvent–solute interactions in binary mixtures of protic–aprotic solvents with different molar ratios. The microenvironment around the solute molecules was observed via electronic absorption spectroscopy and was analyzed by employing solvation models and quantum chemical calculations. The spectral analysis suggested that the solute was preferentially solvated by the polar protic solvent, indicating a lack of synergy between the two solvents. The solvation microsphere was progressively occupied by the protic solvent, on the basis of specific solute–solvent interactions. By modeling the 1:2 (solute-coordinating solvent) complexes with explicit solvents, the binding energy for complex formation was estimated. Full article

Traditional routing algorithms optimizing for distance or travel time are inadequate for electric vehicles (EVs), which require energy-aware planning considering battery constraints and charging infrastructure. This work presents an energy-optimal routing system for EVs that integrates personalized consumption modeling with real-time environmental data. The system employs a Long Short-Term Memory (LSTM) neural network to predict State-of-Charge (SoC) consumption from real-world driving data, learning directly from spatiotemporal features including velocity, temperature, road inclination, and traveled distance. Unlike physics-based models requiring difficult-to-obtain parameters, this approach captures nonlinear dependencies and temporal patterns in energy consumption. The routing framework integrates static map data, dynamic traffic conditions, weather information, and charging station locations into a weighted graph representation. Edge costs reflect predicted SoC drops, while node penalties account for traffic congestion and charging opportunities. An enhanced A* algorithm finds optimal routes minimizing energy consumption. Experimental validation on a Nissan Leaf shows that the proposed end-to-end SoC estimator significantly outperforms traditional approaches. The model achieves an RMSE of 36.83 and an $R^2$ of 0.9374, corresponding to a 59.91% reduction in error compared to physics-based formulas. Real-world testing on various routes further confirms its accuracy, with a Mean Absolute Error in the total route SoC estimation of 2%, improving upon the 3.5% observed for commercial solutions. Full article

The electrification of transport is rapidly reshaping power distribution networks, introducing new technical, regulatory, and operational challenges for Distribution System Operators (DSOs). This article presents an international review of electromobility integration strategies, analyzing experiences from Europe, Canada, Australia, and Greece. It examines how DSOs address grid impacts through smart charging, vehicle-to-grid (V2G) services, and demand flexibility mechanisms, alongside evolving regulatory and market frameworks. European initiatives—such as Germany’s Energiewende and the UK’s Demand Flexibility Service—demonstrate how coordinated planning and interoperability standards can transform electric vehicles (EVs) into valuable distributed energy resources. Case studies from Canada and Greece highlight region-specific challenges, such as limited access in remote communities or island grid constraints, while Australia’s high PV penetration offers unique opportunities for PV–EV synergies. The findings emphasize that DSOs must evolve into active system operators supported by digitalization, flexible market design, and user engagement. The study concludes by outlining implementation barriers, policy implications, and a roadmap for DSOs. Full article

Our Sun is the closest X-ray astrophysical source to Earth. As such, it makes for a strong case study to better understand astrophysical processes. Solar flares are particularly interesting as they are linked to coronal mass ejections as well as magnetic field reconnection sites in the solar atmosphere. Flares can therefore provide insightful information on the physical processes at play on their production sites but also on the emission and acceleration of energetic charged particles towards our planet, making it an excellent forecasting tool for space weather. While solar flares are critical to understanding magnetic reconnection and particle acceleration, their hard X-ray polarization—key to distinguishing between competing theoretical models—remains poorly constrained by existing observations. To address this, we present the CUbesat Solar Polarimeter (CUSP), a mission under development to perform solar flare polarimetry in the 25–100 keV energy range. CUSP consists of a 6U-XL platform hosting a dual-phase Compton polarimeter. The polarimeter is made of a central assembly of four 4 × 4 arrays of plastic scintillators, each coupled to multi-anode photomultiplier tubes, surrounded by four strips of eight elongated GAGG scintillator bars coupled to avalanche photodiodes. Both types of sensors from Hamamatsu are, respectively, read out by the MAROC-3A and SKIROC-2A ASICs from Weeroc. In this manuscript, we present the preliminary spectral performances of single plastic and GAGG channels measured in a laboratory using development boards of the ASICs foreseen for the flight model. Full article

Semiconductor technologies are susceptible to radiation effects. The particle incidence in susceptible areas of an integrated circuit (IC) can generate physical interactions capable of producing errors. This paper predicts the IC cross sections for Single Event Effects. The cross section is a metric that provides an IC’s susceptibility to radiation. It deals with particle source interaction and physical design volumes. This work evaluates the IC cross section, exploring the physical design characteristics of susceptible regions in logic gates. It explores particles with low LET, identifying the charge collection areas. Also, the heavy ions are used to evaluate the critical cross section range. Distinct benchmark circuits were simulated to characterize sensitivity trends. The influence of circuit input conditions along with cells’ susceptibility reveals significant findings. The results indicate a difference up to ten times between low- and high-energy particles. Consequently, predicting the IC cross section at an early stage of the design flow is essential, especially for electronics devices used in radiation environments. Full article

In the pursuit of cleaner and more efficient internal combustion engines, dual-fuel strategies combining diesel and hydrogen are gaining increasing attention. This study employs detailed computational fluid dynamics (CFD) simulations to investigate the behaviour of pilot diesel injections in dual-fuel diesel–hydrogen engines. The study aims to characterize spray formation, ignition delay and early combustion phenomena under various energy input levels. Two combustion models were evaluated to determine their performance under these specific conditions: Tabulated Well Mixed (TWM) and Representative Interactive Flamelet (RIF). After an initial numerical validation using dual-fuel constant-volume vessel experiments, the models are further validated using in-cylinder pressure measurements and high-speed natural combustion luminosity imaging acquired from a large-bore optical engine. Particular attention was given to ignition location due to its influence on subsequent hydrogen ignition. Results show that both combustion models reproduce the experimental behavior reasonably well at high energy input levels (EILs). At low EILs, the RIF model better captures the ignition delay; however, due to its single-flamelet formulation, it predicts an abrupt ignition of all available premixed charge in the computational domain once ignition conditions are reached in the mixture fraction space. Full article

The trend of the world to electric mobility and the inclusion of renewable energy requires complex control and predictive models of Smart Electric Vehicle Charging Stations (SEVCSs). The paper describes an experimental artificial intelligence (AI) model that can be used to optimize EV charging in New York City based on ten years of historical load and weather information. Nonlinear environmental relationships with urban energy demand and the use of Neural Fitting and Regression Learner models in MATLAB were used to explore the nonlinear relationships between the environment and energy demand. The quality of the input data was maintained with a lot of preprocessing, such as outlier removal, smoothing, and time alignment. The performance measurements showed that there was a Mean Absolute Percentage Error (MAPE) of 4.9, and a coefficient of determination (R²) of 0.93, meaning that there was a high level of concordance between the predicted and measured load profiles. Such findings indicate that AI-based models can be used to replicate load dynamics during renewable energy variability. The research combines the findings of long-term and multi-source data with the short-term forecasting to address the research gaps of past studies that were limited to a few small datasets or single-variable-based time series, which will provide a replicable base to develop energy-efficient and intelligent EV charging networks in line with future grid decarbonization goals. The proposed neural network had an R² = 0.93 and RMSE = 36.4 MW. The Neural Fitting model led to less RMSE than linear regression and lower MAPE than the persistence method by a factor of about 15 and 22 percent, respectively. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article