Search Results (314)

Aerodynamic manoeuvring technology for spacecraft actively utilizes aerodynamic forces to alter orbital trajectories. This approach not only substantially reduces propellant consumption but also expands the range of accessible orbits, representing a key technological pathway to address the demands of increasingly complex yet cost-effective space missions. The theoretical prototype of this technology was proposed by Howard London. Over the course of more than half a century of development, it has evolved into four distinct modes: aeroglide, aerocruise, aerobang, and aerogravity assist. These modes have been engineered and applied in scenarios such as in-orbit manoeuvring of reusable vehicles, rapid response to space missions, and interplanetary exploration. Our research centers on two core domains: trajectory optimization and control guidance. Trajectory optimization employs numerical methods such as pseudo-spectral techniques and sequential convex optimization to achieve multi-objective optimization of fuel and time under constraints, including heat flux and overload. Control guidance focuses on standard orbital guidance and predictive correction guidance, progressively evolving into adaptive and robust control to address atmospheric uncertainties and the challenges of strong nonlinear coupling. Although breakthroughs have been achieved in deep-space exploration missions, critical challenges remain, including constructing high-fidelity models, enhancing real-time computational efficiency, ensuring the explainability of artificial intelligence methods, and designing integrated framework architectures. As these technical hurdles are progressively overcome, this technology will find broader engineering applications in diverse space missions such as lunar return and in-orbit servicing, driving continuous innovation in the field of space dynamics and control. Full article

To address grid security issues such as load fluctuation and transformer overloading caused by increasing community EV charging demand, this study proposes two active demand response mechanisms to encourage users to voluntarily participate in orderly charging: a single-signup mechanism and a hybrid mechanism integrating signing willingness with user satisfaction. A hierarchical user satisfaction model is developed, integrating incentive perception and dispatch satisfaction, to characterize nonlinear user responses under varying incentive and dispatch levels. A genetic algorithm is then applied to determine the optimal contract portfolio that maximizes community-wide satisfaction. Simulation results show that the hybrid mechanism achieves the highest average satisfaction (0.8788), significantly outperforming both the single-signup and traditional passive schemes, effectively enhancing user participation and grid flexibility. This study provides a new theoretical framework and optimization pathway for mechanism innovation in orderly electric vehicle charging under centralized construction and unified operation scenarios in residential communities and offers valuable insights for the coordinated development of vehicle–grid interaction and demand-side management models in community-based new power systems. Full article

This article presents an analysis of the effects of braking system damage on the course of the vehicle collision and driving safety. Research was conducted using simulation methods in the V-SIM 7.0 environment, analysing the collision between a car and a truck at three speeds—50, 60, and 70 km/h—under the assumption of a braking system malfunction in the car. The obtained results showed that as the speed of the truck increased, the total kinetic energy of the system nearly doubled, resulting in deformation of the vehicle’s body front of up to 0.6 m. The maximum force acting on the car decreased with increasing speed, which was due to the change in the point of impact. The recorded acceleration values of the car indicate a moderate level of overloads, which should not cause serious injuries to the passengers but do suggest significant stress on the vehicle’s load-bearing structure. The research may serve as a foundation for further work on braking system diagnostics, the development of friction materials, and the modelling of energy absorption processes in collisions involving vehicles of varying mass and geometry. Full article

As an active flow-control technology, the counterflowing jet can reduce drag by reconstructing the flow field structure during the re-entry of a vehicle, thereby mitigating the adverse effects of high overload on personnel. However, variations in the angle of attack (AoA) and nozzle mass flow rate tend to induce transitions in its flow field modes and fluctuations in drag reduction performance. To further investigate the aerodynamic interference characteristics of the counterflowing jet during the re-entry process, this study focused on a 2.6% subscale model of the Apollo return capsule. The Reynolds-averaged Navier–Stokes (RANS) equations turbulence model was employed to numerically analyze the effects of different mass flow rates and freestream AoAs on the flow field modes and the drag behavior. The results indicate that with an increase in AoA, the flow field structure of the long penetration mode (LPM) is likely to be destroyed, and the shock wave shape exhibits significant asymmetric distortion. In contrast, the flow field structure of the short penetration mode (SPM) remains relatively stable; however, the bow shock and Mach disk exhibit two typical offset patterns, whose offset characteristics are jointly regulated by the mass flow rate and AoA. In terms of drag characteristics, the AoA significantly weakens the drag reduction effect of the LPM. In contrast, the SPM can maintain a stable drag reduction efficiency of approximately 50% within a certain AoA range. Nevertheless, as the AoA further increases, the drag reduction effect of the SPM gradually diminishes. Full article

Electric vehicle (EV) charging behavior exhibits strong spatio-temporal randomness, often leading to transient peak loads and an elevated risk of distribution network overloads. In addition, existing prediction models still face challenges in achieving high accuracy, computational efficiency, and effective modeling of multi-level periodic patterns. To address these issues, this study proposes a novel architecture termed the Convolutional Sparse Periodic Transformer Network (CSPT-Net). At the front end of the architecture, the model incorporates a one-dimensional convolutional neural network (1D-CNN) to efficiently capture local temporal features. To improve computational efficiency, the traditional global attention mechanism is replaced with a sparse attention module. Furthermore, a customized periodic time-encoding module is designed to explicitly represent multi-scale temporal regularities such as daily, weekly, and holiday cycles. Extensive experiments on a large-scale dataset containing more than 70,000 real-world charging records demonstrate that CSPT-Net achieves state-of-the-art performance across all evaluation metrics. Specifically, CSPT-Net reduces the Mean Absolute Error (MAE) to 12.21 min and enhances training efficiency by over 58% compared with the standard Transformer baseline. These results confirm that CSPT-Net effectively balances predictive accuracy and computational efficiency while demonstrating superior robustness and generalization in complex real-world environments. Consequently, the proposed framework offers a reliable and high-performance data-driven foundation for power grid load management and charging infrastructure planning. Full article

The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article

To overcome the challenges of low positioning accuracy and inefficient resource utilization in cooperative target localization by unmanned aerial vehicles (UAVs) in complex environments, this paper presents a cooperative localization algorithm that integrates multimodal data fusion with dynamic resource optimization. By leveraging a cross-modal attention mechanism, the algorithm effectively combines complementary information from visual, radar, and lidar sensors, thereby enhancing localization robustness under occlusions, poor illumination, and adverse weather conditions. Furthermore, a real-time resource scheduling model based on integer linear programming is introduced to dynamically allocate computational and communication resources, which mitigates node overload and minimizes resource waste. Experimental evaluations in scenarios including maritime search and rescue, urban occlusions, and dynamic resource fluctuations show that the proposed algorithm achieves significant improvements in positioning accuracy, resource efficiency, and fault recovery, demonstrating strong potential for applications in complex tasks, demonstrating its potential as a viable solution for low-cost UAV swarm applications in complex environments. Full article

The umbilical cable plays a critical role in deep-sea mining systems by connecting the surface support vessel to the mining vehicle. If the spatial configuration of the umbilical cable is unsuitable for mining vehicle operations, it may experience overloading, slack, seabed contact, or be run over by the mining vehicle. To address these issues, this study focuses on double-stepped, steep-wave, and S-shaped configurations and develops a coupled dynamic model of the surface support vessel–umbilical–mining vehicle system using the lumped-mass method, which incorporates hydrodynamic loads induced by currents and irregular waves, as well as motion excitations from the surface support vessel. The spatial configurations and mechanical responses of three umbilical configurations were evaluated, including maximum effective tension, lateral drift amplitude, and the mining vehicle’s overturning moment. The results indicate that the double-stepped configuration offers superior performance in terms of ground clearance, effective tension, and collaborative operation of the mining vehicle, although it faces an increased risk of fatigue failure due to dual buoyancy sections. The S-shaped configuration exhibits improved control of lateral drift and bending response under ocean current excitation, while the steep-wave configuration demonstrates intermediate behavior. In addition, the study analyzed the local compression of the umbilical cable and the variation trends of the mining vehicle’s overturning moments. These findings offer insights into the optimization of umbilical design and operational parameters, enhancing the safety, reliability, and efficiency of deep-sea mining systems. Full article

The rapid growth of electric vehicles (EVs) requires the deployment of modular fast charging stations that balance charging performance, grid limitations, and investment costs. This study develops design scenarios for modular EV fast charging stations and introduces a risk-aware performance analysis framework under power and grid quality constraints. A simulation-based approach evaluates 286 station configurations with ten charging outlets (20–50 kW), grouped into 16 representative classes based on three key dimensions: total installed power, dominant charger type, and peak load risk. Performance metrics such as efficiency of charger utilization, load factor, and overload risk are used to construct Pareto frontiers and identify optimal trade-offs between capacity and operational safety. Results indicate that medium-power configurations (251–350 kW) achieve the best compromise between efficiency (>82%) and load factor (>50%) without exceeding safe operating limits, while high-power configurations enable maximum throughput at the expense of elevated overload risk. Sensitivity analysis confirms the robustness of the proposed grouping approach under variations in arrival rates, battery sizes, and grid constraints (400–600 kW). The findings provide practical insights into the design and risk management of modular charging stations, supporting urban planners and power engineers in developing efficient and reliable EV charging infrastructure. Full article

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

With the increasing frequency of extreme rainfall events, pluvial flooding has become a critical challenge to the safety and sustainable development of megacities worldwide. This study proposes a multi-dimensional framework for assessing urban pluvial flood resilience (UPFR) by integrating a coupled hydrological-hydrodynamic model with system performance curves. The framework characterizes the dynamic evolution of resilience across three dimensions: rainfall characteristics, risk thresholds, and spatial scales. Results show that short-duration intense rainfall triggers instantaneous pipe overloading, whereas long-duration storms impose cumulative stress that leads to sustained systemic weakening, with the lowest resilience observed under extreme prolonged rainfall conditions. The specification of risk thresholds strongly influences resilience ranking, with the vehicle stalling risk (VSR) consistently showing the lowest resilience, followed by building inundation risk (BIR) and human instability risk (HIR). Spatially, pipes represent the weakest components, nodes maintain resilience under moderate stress, and the regional system exhibits a pattern of local weakness but overall stability, accompanied by delayed recovery. These findings highlight the importance of incorporating multi-threshold and multi-scale perspectives in flood resilience assessment and management. The proposed framework provides a scientific basis to support staged prevention measures and adaptive emergency response strategies, thereby enhancing urban flood resilience in megacities. Full article

Despite continued efforts by the Korean government to improve road safety, truck-related accidents remain disproportionately fatal, with a rate approximately 2.6 times higher than that of passenger vehicles. Although legal regulations prohibit overloading and improper loading, existing enforcement practices—primarily dependent on low-speed weigh-in-motion (WIM) systems—are limited in coverage and responsiveness. This study develops and validates standardized test procedures for detecting overloading and improper loading in commercial freight vehicles using a high-speed weigh-in-motion (HS-WIM) system. The HS-WIM system offers advanced sensing capabilities, including vehicle speed, length, axle configuration, and weight measurement at highway speeds. However, Korean HS-WIM performance standards currently lack detailed guidance, especially concerning group axle load testing and asymmetric cargo detection. To address these regulatory and technical gaps, a comprehensive set of test scenarios was designed based on domestic and international standards. A dedicated testbed was constructed, and 12 commercial vehicle types were tested under varied speeds and loading conditions. The proposed procedures reliably detect violations, and the study introduces evaluation criteria that improve HS-WIM system accuracy and support future enforcement and policy development in Korea. Full article

This study proposes an innovative stacking ensemble learning framework for classifying faults in induction motors utilized in Electric Vehicles (EVs). Employing a comprehensive dataset comprising motor data, such as speed, torque, current, and voltage, the analysis encompasses six distinct conditions: normal operating mode, over-voltage fault, under-voltage fault, overloading fault, phase-to-phase fault, and phase-to-ground fault. The proposed model integrates Gradient Boosting (GB), K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms in a synergistic manner. The findings reveal that the RF–GB–DT–XGBoost combination achieves a remarkable accuracy of 98.53%, significantly surpassing other methods reported in the literature. Performance is evaluated through metrics including accuracy, precision, sensitivity, and F1-score, with results analyzed in comparison to practical applications and existing studies. Validated with real-world data, this study demonstrates that the proposed model offers a groundbreaking solution for predictive maintenance systems in the EV industry, exhibiting high generalization capacity despite complex operating conditions. This approach holds transformative potential for both academic research and industrial applications. The dataset used in this study was generated using a MATLAB 2018/Simulink-based Variable Frequency Drive (VFD) model that emulates real-world EV operating conditions rather than relying solely on laboratory data. This ensures that the developed model accurately reflects practical electric vehicle environments. Full article

Germany’s energy transition to a higher share of renewable energy sources (RESs) is characterized by decentralization, with citizens, cooperatives, SMEs, and municipalities playing a central role. As of early 2025, private individuals own a significant share of renewable energy installations, particularly PV panels, which corresponds to approximately half of the total installed PV power. This trend is driven by physical, technological, and societal factors. Technological advances in battery storage and sector coupling are expected to further decentralize the energy system. Thereby, the electrification of mobility, particularly through electric vehicles (EVs), offers significant storage potential and grid-balancing capabilities via bidirectional charging, although it also introduces challenges, especially for distribution grids during peak loads. Within this work we present a detailed digital twin of the entire distribution grid of the rural German municipality of Wüstenrot. Using grid operator data and transformer measurements, we evaluate strategic expansion scenarios for electromobility, PV and heat pumps based on existing infrastructure and predicted growth in both public and private sectors. A core focus is the intelligent integration of EV charging infrastructure to avoid local overloads and to optimise grid utilisation. Thereby municipally planned and privately driven expansion scenarios are compared, and grid bottlenecks are identified, proposing solutions through charge load management and targeted infrastructure upgrades. This study of Wüstenrot’s low-voltage grid reveals substantial capacity reserves for future integration of heat pumps, electric vehicles (EVs), and photovoltaic systems, supporting the shift to a sustainable energy system. While full-scale expansion would require significant infrastructure investment, mainly due to widespread EV adoption, simple measures like temporary charge load reduction could cut grid stress by up to 51%. Additionally, it is shown that bidirectional charging offers further relief and potential income for EV owners. Full article

The transformation of transport towards solutions based on renewable energy sources (RES) and energy storage systems represents a response to global climate and regulatory challenges. The integration of electric vehicles with charging infrastructure and the power grid reduces emissions and enhances system flexibility; however, it simultaneously introduces new areas of risk and should therefore be subject to significance assessment. This study applies an integrated methodology for assessing the significance of changes, combining FMEA-based analysis with risk registers and sustainability indicators (six criteria). The transport system and associated storage infrastructure were compared before and after the implementation of RES, considering criteria such as the effects of system failure, complexity, innovation, monitoring, reversibility, and additionality. The results indicate that traditional risks associated with fossil fuels (e.g., exhaust emissions, pipeline failures) are eliminated, but new risks emerge. The highest increases in Risk Priority Numbers (RPN) were observed for cyber threats, charging infrastructure overloads, and the cyclic degradation of energy storage systems. Environmental and organizational risks also intensified, including those related to battery recycling as well as the lack of regulatory frameworks and procedures. The integration of transport with RES and energy storage should be regarded as a significant change. In addition to environmental and energy benefits, it introduces new, complex risk areas that require in-depth risk analysis, the implementation of monitoring systems, and adequate regulatory and preventive measures. At the same time, the proposed methodology enables the identification of changes critical to power system stability, the improvement of energy efficiency, and the advancement of the transition towards climate neutrality. Full article