Search Results (6,191)

Considering the nonlinear trends, multi-scale variations, and capacity regeneration phenomena exhibited by battery capacity degradation under real-world conditions, accurately predicting its trajectory remains a critical challenge for ensuring the reliability and safety of electric vehicles. To address this, this study proposes a hybrid prediction framework based on Variational Mode Decomposition and a Transformer–Long Short-Term Memory architecture. Specifically, the proposed Variational Mode Decomposition–Transformer for Time Series–Long Short-Term Memory (VMD–TTS–LSTM) framework first decomposes the capacity sequence using Variational Mode Decomposition. The resulting modal components are then aggregated into high-frequency and low-frequency parts based on their frequency centroids, followed by targeted feature analysis for each part. Subsequently, a simplified Transformer encoder (Transformer for Time Series, TTS) is employed to model high-frequency fluctuations, while a Long Short-Term Memory (LSTM) network captures the long-term degradation trends. Evaluated on charging data from 20 commercial electric vehicles under a long-horizon setting of 20 input steps predicting 100 steps ahead, the proposed method achieves a mean absolute error of 0.9247 and a root mean square error of 1.0151, demonstrating improved accuracy and robustness. The results confirm that the proposed frequency-partitioned, heterogeneous modeling strategy provides a practical and effective solution for battery health prediction and energy management in real-world electric vehicle operation. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Very Low Earth Orbit (VLEO) satellites, operating at altitudes below 450 km, provide tremendous potential in the domain of remote sensing. Their proximity to Earth offers high resolution, low latency, and rapid revisit rates, allowing continuous monitoring of dynamic systems and real-time delivery of vertically integrated earth observation products. Nonetheless, the application of VLEO is not yet fully realized due to numerous complexities associated with VLEO satellite development, considering atmospheric drag, short satellite lifetimes, and social, political, and legal regulatory fragmentation. This paper reviews the recent technological developments supporting sustainable VLEO operations with regards to aerodynamic satellite design, atomic oxygen barriers, and atmospheric-breathing electric propulsion (ABEP). Furthermore, the paper provides an overview of the identification of regulatory and economic barriers that extort additional costs for VLEO ranging from frequency band allocation and space traffic management to life-cycle cost and uncertain commercial demand opportunities. Nevertheless, the commercial potential of VLEO operations is widely acknowledged, and estimated to lead to an economic turnover in the order of 1.5 B USD in the next decade. Learning from the literature and prominent past experiences such as the DISCOVERER and CORONA programs, the study identifies key gaps and proposes a roadmap to sustainable VLEO development. The proposed framework emphasizes modular and serviceable satellite platforms, hybrid propulsion systems, and globally harmonized governance in space. Ultimately, public–private partnerships and synergies across sectors will determine whether VLEO systems become part of the broader space infrastructure unlocking new capabilities for near-Earth services, environmental monitoring, and commercial innovation at the edge of space. Full article

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

As distribution networks become increasingly intelligent, primary–secondary integrated distribution switches are replacing the traditional electromagnetic type. However, the high degree of integration intensifies inherent electromagnetic compatibility (EMC) challenges. This paper presents a field-circuit hybrid full-wave model to investigate switch characteristics during lightning strikes. A 3D full-wave model of the switch and a distributed parameter circuit model of the connecting lines are coupled via a network parameter matrix. This approach comprehensively accounts for the impacts of transmission lines and structural components on electromagnetic disturbances. Simulation and experimental results reveal that lightning strikes induce high-frequency damped oscillatory waves, primarily caused by traveling wave reflections along overhead lines. The characteristic frequency of disturbance is inversely proportional to the transmission line length. Additionally, internal components significantly influence this frequency; specifically, a larger voltage dividing capacitance in the voltage transformer results in a lower frequency. Model validation was performed using a 20 m transmission line setup. A 75 kV standard lightning impulse was injected into Phase B. At a distance of 500 mm from the voltage transformer, the measured radiated electric field amplitude was 14.12 kV/m (deviation < 5%), and the characteristic frequency was 1.11 MHz (deviation < 20%). These findings offer vital guidance for the lightning protection and EMC design of primary–secondary integrated distribution switches. Full article

Solar-aided power generation offers a pathway to reduce the carbon dioxide emissions from existing coal-fired plants. This study addresses the gap in comparing different solar integration modes by conducting a thermo-economic analysis of a 600 MW coal-fired system retrofitted with a solar tower. Four integration modes were designed and rigorously compared, encompassing series and parallel configurations at either the high-exergy reheater or the lower-exergy economizer. A detailed thermodynamic model was developed to simulate its off-design and annual performance. The results showed that integration at the primary reheater outperformed the economizer integration. Specifically, the parallel configuration at the primary reheater (Mode II) achieved the highest annual solar-to-electricity efficiency of 18.43% at a thermodynamically optimal heliostat field area of 125,025.6 m². Economic analysis revealed a trade-off, with the minimum levelized cost of energy (LCOE) of −0.00929 USD/kWh for Mode II occurring at the economically optimal area of 321,494 m² due to greater coal and emission savings. Sensitivity analysis across two other locations confirmed that the annual solar-to-electricity efficiency and LCOE are directly influenced by solar resource quality, but the thermodynamically optimal and economically optimal heliostat field area remain consistent. This work demonstrates that parallel integration with the primary reheater presents a favorable and practical configuration, balancing high solar-to-electricity conversion efficiency with favorable economics for hybrid solar–coal power plants. Full article

The growing demand for electric vehicles, renewable energy systems, and portable electronics has led to the widespread adoption of power conversion systems. Although advanced structures like the superjunction MOSFET have prolonged the viability of silicon in power applications, maintaining its dominance through cost efficiency, Si-based technology is ultimately constrained by its intrinsic limitations in critical electric fields. To address these constraints, research into wide bandgap semiconductors aims to minimize system footprint while maximizing efficiency. This study reviews the semiconductor landscape, demonstrating why Gallium Nitride (GaN) has emerged as the most promising technology for next-generation power applications. With a critical electric field of $3.75\mathrm{MV}/\mathrm{cm}$ ($12.5\times$ higher than Si), GaN facilitates power devices with lower conduction loss and higher frequency capability when compared to their Si counterpart. Furthermore, this paper surveys the GaN ecosystem, ranging from device modeling and packaging to monolithic ICs and switching converter implementations based on discrete transistors. While existing literature primarily focuses on discrete devices, this work addresses the critical gap regarding GaN monolithic integration. It synthesizes key challenges and achievements in the design of GaN integrated circuits, providing a comprehensive review that spans semiconductor technology, monolithic circuit architectures, and system-level applications. Reported data demonstrate monolithic stages reaching $30\mathrm{m}\Omega$ and $25\mathrm{MHz}$, exceeding Si performance limits. Additionally, the study reports on high-density hybrid implementations, such as a space-grade POL converter achieving $123.3\mathrm{kW}/\mathrm{L}$ with $90.9\%$ efficiency. Full article

Ocean current energy, owing to its predictability and stability, is regarded as an ideal power source for distributed marine observation networks and underwater intelligent equipment. However, conventional ocean current energy devices that rely on rigid turbines and electromagnetic generators generally suffer from high cut-in flow velocity, bulky size, high maintenance costs, and significant environmental disturbance, making them unsuitable for deep-sea, miniaturized, and long-duration power supply scenarios. These limitations highlight the urgent need for flexible and low-speed energy harvesters capable of autonomous, long-term operation. In recent years, nanogenerator technology has provided new opportunities for distributed and low-power ocean current energy harvesting. PENGs and TENGs can directly convert weak mechanical energy into electricity, enabling energy harvesting in small-scale and low-velocity flow fields. PENGs offer high durability and mechanical robustness, whereas TENGs exhibit superior output performance in low-speed and intermittent flows. This paper provides a comprehensive review of structural designs, material innovations, interface engineering, hybrid energy-conversion architectures, and power-management strategies for PENG- and TENG-based ocean current energy harvesters. Overall, future progress will rely on the integration of intelligent materials, multi-field coupling mechanisms, and system-level engineering strategies to achieve durable, scalable, and autonomous ocean current energy harvesting for distributed marine systems. Full article

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

Decarbonisation of the aviation sector is essential for achieving global-climate targets, with hydrogen propulsion emerging as a viable alternative to battery–electric systems for vertical flight. Unlike previous studies focusing on clean-sheet eVTOL concepts or fixed-wing platforms, this work provides a comprehensive retrofit evaluation of a two-seat light helicopter (Cabri G2/Robinson R22 class) to a hydrogen–electric hybrid powertrain built around a Toyota TFCM2-B PEM fuel cell (85 kW net), a 30 kg lithium-ion buffer battery, and 700 bar Type-IV hydrogen storage totalling 5 kg, aligned with the Vertical Flight Society (VFS) mission profile. The mass breakdown, mission energy equations, and segment-wise hydrogen use for a 100 km sortie are documented using a single main rotor with a radius of R = 3.39 m, with power-by-segment calculations taken from the team’s final proposal. Screening-level simulations are used solely for architectural assessment; no experimental validation is performed. Mission analysis indicates a 100 km operational range with only 3.06 kg of hydrogen consumption (39% fuel reserve). The main contribution is a quantified demonstration of a practical retrofit pathway for light rotorcraft, showing approximately 1.8–2.2 times greater range (100 km vs. 45–55 km battery-only baseline, including respective safety reserves). The Hawk demonstrates a 28% reduction in total propulsion system mass (199 kg including PEMFC stack and balance-of-plant 109 kg, H₂ storage 20 kg, battery 30 kg, and motor with gearbox 40 kg) compared to a battery-only configuration (254.5 kg battery pack, plus equivalent 40 kg motor and gearbox), representing approximately 32% system-level mass savings when thermal-management subsystems (15 kg) are included for both configurations. Full article

Studies on two-dimensional materials (such as topological insulators or transition metal dichalcogenides) have shown that they exhibit unique properties, including high charge carrier mobility and tunable bandgaps, making them attractive for next-generation electronics. Some of these materials (e.g., HfSe${}_2$) also offer thickness-dependent bandgap engineering. However, the standard device fabrication techniques often introduce processing contamination, which reduces device efficiency. In this paper, we present a modified mechanical exfoliation technique, the Reversed Structuring Procedure, which enables the fabrication of hybrid systems based on 2D microflakes with improved interface cleanness and contact quality. Hall effect measurements on Bi${}_2$Se${}_3$ and HfSe${}_2$ devices confirm enhanced electrical performance, including the decrease in the measured total resistance. We also introduce a novel Star-Shaped Electrode Structure, which allows for accurate Hall measurements and the exploration of geometric magnetoresistance effects within the same device. This dual-purpose geometry enhances the flexibility and demonstrates broader functionality of the proposed fabrication method. The presented results validate the Reversed Structuring Procedure method as a robust and versatile approach for laboratory test-platforms for electronic applications of new types of layered materials whose fabrication technology is not yet compatible with CMOS. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

The increasing penetration of renewable energy has led to a decrease in system inertia, challenging grid stability and frequency regulation. This paper presents a dynamic estimation framework for load-side virtual inertia, supported with high-power-density electrical double-layer supercapacitors (EDLCs). By leveraging the fast response and high power density of EDLCs, the proposed method enables the real-time emulation of demand-side inertial behavior, enhancing frequency support capabilities. A hybrid estimation algorithm has been developed that combines demand forecasting and adaptive filtering to track virtual inertia parameters under varying load conditions. Simulation results, based on a 150 kVA distributed system with 27% renewable penetration and 33% demand variability, demonstrate the effectiveness of the approach in improving transient stability and mitigating frequency deviations within ±0.1 Hz. The integration of ESS-based support offers a scalable and energy-efficient solution for future smart grids, ensuring operational reliability under real-world variability. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article