Search Results (2,659)

Nowadays, numerical simulation methods are advanced and widely used in industry, enabling the modeling of complex systems from printed circuit boards (PCBs) to full power converters. Among many isolated topologies, the phase-shift full-bridge (PSFB) topology is a well-established solution for isolated DC–DC conversion in electric vehicles. Therefore, this paper proposes a broadband electromagnetic compatibility (EMC) modeling methodology for a custom-designed 1 kW gallium nitride (GaN)-based PSFB converter intended for an electric vehicle (EV) DC powertrain. Moreover, the approach combines full-wave electromagnetic simulation with circuit-level simulation, including parasitic effects from PCB layout, power harnesses, and discrete components. Thus, the virtual prototype is assessed within a complete virtual test bench compliant with the standard Comité International Spécial des Perturbations Radioélectriques (CISPR) 25 over the 150 kHz–108 MHz range to capture common-mode (CM) and differential-mode (DM) conducted electromagnetic interference (EMI). Results show that the converter achieves efficiencies of 97.26% in standalone mode and 97.03% when integrated into the full DC powertrain. However, the conducted EMI assessment reveals that both CM and DM emissions exceed CISPR 25 Class 2 limits across the entire spectrum, with excess levels reaching up to 72 dBµV. Therefore, power harnesses significantly increase EMI levels at low frequencies due to the distributed inductance and stray capacitance. Finally, this study demonstrates the value of virtual prototyping for simulation-based EMI prediction in early-stage power converter design. Full article

The growing penetration of renewable energy has intensified building load fluctuations, substantially increasing balancing costs. Electric vehicles (EVs) in building clusters often have considerable idle parking time beyond essential charging needs, enabling them to provide significant flexibility while meeting scheduled demands. This EV flexibility can balance intra-day load deviations and enable arbitrage in day-ahead electricity markets. However, conventional model-based approaches are fundamentally limited by their dependence on forecasting accuracy under high uncertainty from renewable generation and EV behavior. To address this, we propose a novel bi-level online optimization framework. The upper level employs a Lyapunov optimization-based algorithm that operates without predictions, making real-time decisions on total EV charging power to balance supply-demand mismatches. The lower level introduces novel flexibility metrics for individual EVs—encompassing temporal, volumetric, and cross-day dimensions—and optimizes power allocation by minimizing flexibility loss. Furthermore, we model EV flexibility as virtual queues and rigorously derive mathematical bounds on their limits, providing theoretical support for managing flexibility reserves. Rigorous analysis validates the framework’s feasibility, and comprehensive simulations demonstrate its superiority over benchmark algorithms, achieving significant cost reductions under various uncertainty scenarios. Full article

Short circuits and subsequent fires resulting from objects impacting the bottom of vehicle power battery packs considerably jeopardize electric vehicle (EV) operations. This study investigated underbody impacts in EVs and the overall mechanical properties of battery cells. Key features of road debris were extracted and simplified to establish a geometric parameter structure model and determine realistic battery pack responses to debris impact. Quasi-static compression and dynamic impact tests on a prismatic lithium-ion battery (LIB) and power battery pack followed. Macroscopic mechanical responses, deformation failure modes, and internal jellyroll damage of cells and packs were evaluated, and constitutive equations and failure parameters were derived to develop a finite element model, whose effectiveness and reliability were verified by comparing simulation results with experimental data. Finally, a homogenized model of the prismatic LIB and power battery pack was constructed, which effectively predicted the macroscopic mechanical response and internal short-circuit failure under mechanical loading. However, simulation and test results revealed certain deviations in cell indentations under battery pack bottom impacts, presumably because the FEMs neglect the dynamic strain rate effects of electrolyte and cooling liquid. Overall, this study elucidates safety risks to cells and their key components under power battery pack bottom impacts. Full article

Low-voltage electrical contacts are core components of power distribution systems, renewable energy installations, and industrial automation equipment. The electric arc generated during contact switching is the primary cause of contact erosion, material transfer, and equipment failure, posing significant threats to system reliability and operational safety. The accurate prediction of arc parameters is hindered by two challenges: the high scatter in available data undermines empirical models, and purely data-driven approaches risk physically implausible results. To address this, a Gaussian Mixture-enhanced Bayesian-optimized Physics-Informed Neural Network (GB-PINN) is proposed. Three core contributions are made: (1) High-fidelity MHD simulation foundation: A magnetohydrodynamic (MHD) multi-physics coupling model of the contact arc was constructed and validated against experiments, showing high fidelity with only 1.63% error in arc duration and 1.82% in arc energy. A multivariate simulation dataset was generated by varying key contact parameters based on this validated model. (2) GMM-based data augmentation: The measured and simulated data were modeled and sampled via Gaussian Mixture Model (GMM) to enrich the dataset while preserving physical consistency. (3) BOHB-optimized PINN prediction: The Bayesian Optimization and Hyperband (BOHB) algorithm was employed to optimize the PINN hyperparameters, enhancing training efficiency and predictive accuracy. Experimental results demonstrated that the proposed GB-PINN achieved superior performance in predicting arc duration and energy, with mean absolute errors (MAE) of 0.079 ms and 0.624 mJ, root mean square errors (RMSE) of 0.099 ms and 0.774 mJ, and coefficients of determination (R²) of 0.980 and 0.979, significantly outperforming grey model (GM (1, N)), long short-term memory (LSTM), and Transformer models. As a physics-informed data-driven tool, GB-PINN enables high-precision arc prediction, providing reliable support for electrical contact design. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

Taking a small long-endurance unmanned surface vehicle (USV) with a trapezoidal cross-section deck structure as the research object, this study investigates the power generation characteristics of a heterogeneous photovoltaic (PV) system consisting of two symmetrically arranged PV arrays with different orientations, under various electrical connection schemes, tilt angles, and heading angles. A PV power prediction model that accounts for dynamic USV attitude changes was established, and the simulation model was validated based on a trapezoidal deck test setup with a tilt angle of 26.6°. Using this model, the daily cumulative energy yields of the independent and parallel configurations were simulated and analyzed under different tilt and heading angles, focusing on the power generation efficiency of the heterogeneous PV system under seakeeping hull constraints. The results show that at a tilt angle of 24°, the daily cumulative energy yield of the heterogeneous system is approximately 95% of that of the horizontal layout, indicating that the trapezoidal frame structure maintains high power generation efficiency while improving wave resistance. The heading angle has only a minor effect on the daily cumulative energy yield, suggesting that variations in course during marine navigation have little impact on power generation. Nevertheless, a significant coupling effect exists between heading angle and tilt angle. Taking a tilt angle of 60° as an example, when the heading increases from 0° to 90°, the energy yield deficit increases from 26.5% to 30.5%. The parallel configuration exhibits slightly lower energy loss at non-south headings and offers a simplified system structure, although its absolute energy yield is marginally lower at large tilt angles. These findings provide practical design guidance for heterogeneous PV systems in sustained ocean observation, climate research, and other long-duration marine missions. Future work will focus on sea trials, hybrid energy integration, and durability studies to further validate and extend these findings. Full article

To alleviate the trade-off between high-speed responses and RF output capability in modified uni-traveling-carrier photodiodes (MUTC-PDs), an MUTC-PD incorporating an electric-field regulation layer (EFRL-MUTC-PD) is proposed. A 20 nm EFRL is inserted between the PD’s collector layer and its cliff layer to tailor the electric-field distribution in the collector layer, thereby enabling electron transport near the peak drift velocity under high-photocurrent operation. Simulation results indicate that the optimal doping concentration of the EFRL is $1\times {10}^{16}$ cm⁻³. For an 8 µm diameter device operated at a bias voltage of −4 V and a photocurrent of 15 mA, the simulation predicts a 3 dB bandwidth of 130 GHz and a transit-time-limited bandwidth of 162 GHz, corresponding to a 9.3% improvement in the simulated 3 dB bandwidth compared with a conventional MUTC-PD. In addition, the simulated RF output power reaches 11.54 dBm at 130 GHz under the adopted simulation assumptions. Full article

In electric vehicles, power batteries consist of numerous individual cells connected in series or parallel. Variations in manufacturing, operating conditions, and aging can lead to differences among these cells. Such inconsistencies can compromise the battery pack’s performance, safety, and overall service life. Therefore, accurately diagnosing inconsistencies among battery cells is of great significance for enhancing the reliability of the battery system and ensuring the operational safety of the vehicle. To address the limited computational resources available in vehicles, this paper proposes an end-cloud collaborative fault diagnosis framework and validates its effectiveness using real-world vehicle driving data. On the cloud side, a deep learning-based reconstruction network is developed to enable high-precision reconstruction of cell voltages. On the vehicle side, a second-order equivalent circuit model is used to represent battery dynamics. An adaptive forgetting factor recursive least squares method is introduced for online estimation of the model parameters, enabling accurate local prediction of individual cell voltages. Using the cloud-reconstructed and vehicle-predicted cell voltages, the extreme difference value of voltage for each cell is computed. A comprehensive diagnosis of inconsistency faults is then performed by fusing the extreme difference in voltage results from both the cloud and vehicle sides via the Extended Kalman Filter (EKF); threshold judgment is conducted based on the fused results, and the Cumulative Sum (CUSUM) algorithm is designed to identify cell inconsistency faults. Experimental results show that the proposed method effectively detects battery inconsistency faults and demonstrates strong engineering applicability and practical potential. Full article

The increasing penetration of photovoltaic (PV) systems in agricultural irrigation poses significant challenges in terms of energy self-sufficiency and operational cost, particularly when installed capacity is insufficient to cover pumping demand. This comparative study evaluates the energy and economic performance of three storage-based configurations applied to a real PV-powered irrigation system, using a PV capacity of 112 kWp as a common baseline. The methodology combines hourly energy balance modelling with linear programming optimization, implemented under both a grid energy minimization objective and a net cost minimization objective, within a model predictive control framework. Three scenarios are compared against a passive reference case: battery storage integration (Scenario 1), reservoir-based hydraulic storage (Scenario 2), and a combined electrical and hydraulic storage configuration (Scenario 3). Results show that system performance is strongly conditioned by the chosen objective function. When self-sufficiency is prioritized, Scenario 3 achieves the greatest reduction in grid imports by combining intraday electrical flexibility with demand rescheduling. When net cost minimization is the primary criterion, Scenario 2 proves most competitive, exploiting pumping flexibility and surplus compensation revenues. These findings highlight that storage technology selection in PV irrigation systems should be driven by the primary operational objective rather than by a single performance indicator. Full article

Electric motor control may be challenging due to nonlinearity, cross-coupling and current and voltage constraints. Multivariable action may enhance the effectiveness of control on electric motors. This work presents the real-time implementation of a Model Predictive Control (MPC) strategy for current regulation in a low-power synchronous electric motor, on a low-cost microcontroller platform. The experimental setup employs a back-to-back configuration with a DC motor operating as a generator, enabling comparative analysis of the impact of different cost-function formulations on the closed-loop dynamics. Both transient and steady-state capabilities have been investigated through suitable key performance indexes. Full article

Slip ring–brush assemblies are widely used in satellite mechanisms to transmit power and signals across rotating interfaces. Under authentic space environments—vacuum, radiation-dominated thermal exchange, and long-duration operation—the coupled effects of mechanical contact dynamics, electrical conduction, intermittent separation, and arcing can accelerate wear and degrade reliability. This paper presents a surface-resolved multiphysics model for multi-track slip rings with staggered brushes. The ring surface is discretized on a circumferential–axial grid and endowed with correlated 3D roughness, enabling interference-based asperity contact. Brush normal dynamics (mass–spring–damper) convert runout and micro-vibration into normal-force ripple and separation events. Electrical conduction is modeled by a parallel admittance network combining pressure-dependent micro-contact conduction and an event-based arc channel activated by separation, opening velocity, and current density with stochastic ignition. A 2D thermal model with ADI integration accounts for Joule/friction heating, radiative cooling, and optional hub conduction. Wear evolves via an Archard-type mechanical term and an arc-energy-driven erosive term. A FAST–MACRO multiscale scheme (20 s FAST, 100 h MACRO with periodic recalibration) enables tractable long-horizon wear prediction while preserving arc statistics. Baseline simulations for a 28 V bus demonstrate rare but nonzero arc activity and predict spatially non-uniform wear at the micrometer scale after 100 h. Full article

This study uses and compares stationary and non-stationary Generalised Extreme Value Distribution (GEVD) to model the behaviour of nitrogen dioxide (NO₂) emission maxima from each of 13 Eskom’s coal-fuelled power stations. The pollutant is modelled to facilitate monitoring and regulation in order to protect public health and the environment. The Maximum Likelihood Estimate (MLE) and Generalised Maximum Likelihood Estimate (GMLE) parameter estimation methods are used and compared in finding the best-fitting model per power station. The results show that a non-stationary model with time-dependent location and/or scale parameter(s) produced the best fit for ten of the power stations, while a stationary model gave the best fit for three, as confirmed by the diagnostic tools. Future extremely high NO₂ emissions were estimated by making use of the 40 and 100 quarter return levels based on the best-fitting models. This study shows how stationarity may not hold for all NO₂ emission data from Eskom’s coal-fired power stations. Modelling data using time-dependent non-stationary GEVD models can be useful, especially in identifying and predicting trends or patterns in worsening high NO₂ emissions with time. This modelling approach is important in providing information for planning and policy formulation of extreme emissions from coal-fired electricity-generating power stations at Eskom (South Africa). Full article

This paper presents a primary side model predictive control (MPC) strategy for wireless power transfer (WPT) based charging of light electric vehicle (LEVs). A battery simulator develops a model to accurately reproduce constant-current (CC) charging profile from Open Ciruit Voltage (OCV) and State of Charge (SoC) parameters of the battery. This model forms the foundation of the predictive control design, allowing accurate prediction of the charging trajectory while avoiding reliance on secondary-side feedback signals. The WPT system employs a phase-shifted full-bridge (PSFB) inverter with S-S compensation, where the primary-side controller regulates the secondary-side charging current using only primary-side current measurements. In contrast to conventional secondary side control, which is tuned around nominal coupling, requires explicit feedback, and degrades under coil misalignment and parameter variations, the proposed MPC leverages integrated system and battery models to predict future states and optimally adjust the phase shift for robust charging operation. Simulation and experimental validation on a real-time LEV charging prototype under aligned, lateral, and angular misalignment conditions demonstrate significant reduction in current-settling time compared to fixed-gain proportional-integral (PI) and known adaptive feedback controllers for same system, with lower RMS current and reduced current spikes at the battery. On the embedded controller, the proposed MPC executes within approximately 1 µs per 85 kHz PWM cycle, corresponding to less than 10% CPU utilization, confirming its practical real-time feasibility. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article