Search Results (841)

The increasing integration of distributed energy resources (DERs), including photovoltaic systems, battery energy storage systems, electric vehicles, and flexible loads, is transforming modern power systems and creating new challenges for coordination, control, and cybersecurity. Conventional Virtual Power Plant (VPP) architectures typically rely on centralized energy management systems, which may face scalability limitations, communication bottlenecks, cybersecurity risks, and reduced resilience to failures. This paper presents a blockchain-enabled decentralized Virtual Power Plant framework for secure and autonomous coordination of distributed energy resources. The proposed architecture combines blockchain technology, smart contracts, IoT-based communication infrastructure, and decentralized energy management functions within a unified multi-layer coordination framework. Smart contracts automate energy scheduling, peer-to-peer transaction validation, and settlement processes, reducing dependence on centralized control entities. Lightweight blockchain consensus mechanisms are employed to improve scalability while limiting computational overhead. The effectiveness of the proposed framework is evaluated through simulation-based case studies involving decentralized DER coordination, peer-to-peer energy trading, and resilience assessment under node-failure conditions. Its performance is compared with that of a conventional centralized VPP architecture in terms of scalability, coordination reliability, communication overhead, transaction transparency, and fault tolerance. The results indicate that the decentralized framework improves operational resilience, coordination transparency, and scalability under increasing DER participation while maintaining satisfactory energy balancing performance. Although blockchain-based coordination introduces additional transaction latency, the proposed approach enhances cybersecurity, reduces dependence on centralized control structures, and provides a flexible foundation for future intelligent smart-grid applications. Full article

Interior permanent magnet synchronous motor (IPMSM) has advantages with high power density, wide speed range, small size, and high efficiency, and is widely used in the drive system of electric vehicles. Compared to other types of motors, permanent magnet synchronous motors (PMSMs) have some irreplaceable advantages, but there are also some disadvantages. As a type of PMSM, IPMSMs have problems with large fluctuations in permanent magnet (PM) magnetic field and demagnetization. At present, irreversible demagnetization of PMs is the most serious problem faced by IPMSMs. Once irreversible demagnetization of PMs occurs, it can cause a decrease in the performance of IPMSMs and can even damage the entire drive system. This paper takes an IPMSM with 48 slots, 8 poles, and 66 kW as the research object. Based on the reasons for PM demagnetization, a PM demagnetization model is established to obtain the demagnetization law of PMs. Firstly, the magnetic properties of PM materials were described based on their characteristic curves. The demagnetization mechanism of PMs was analyzed, and the demagnetization process of PMs was studied in combination with the reasons for demagnetization. Secondly, the basic parameters and torque performance of IPMSMs were calculated and analyzed. We analyzed the demagnetization curves of PM materials at different temperatures, calculated the operating points of PMs under various working conditions, and analyzed whether PMs undergo irreversible demagnetization based on the relationship between the operating points of PMs and the knee points of demagnetization curves. A high-fidelity electromagnetic–thermal coupling simulation model has been established, combined with the characteristics of electric vehicle driving conditions, to accurately characterize the temperature rise distribution and electromagnetic parameter changes of IPMSMs under different operating conditions and achieve multi-physics field collaborative analysis. Finally, a finite element model is adopted to simulate uniform and local demagnetization of PMs, and the changing characteristics of motor performance parameters under demagnetization are summarized. Different magnitudes of d-axis reverse current are applied as demagnetization excitation to analyze PM behaviors under various demagnetization degrees. The variations in magnetic flux density, output torque, and no-load back electromotive force (EMF) before and after demagnetization are simulated and analyzed. For the investigated motor and specific magnet grade, this work summarizes the irreversible demagnetization characteristics and corresponding practical judgment references. Full article

Accurate forecasting of urban electric vehicle (EV) charging demand is important for power system operation, sustainable transport electrification, and charging infrastructure planning. However, this task remains challenging because EV charging demand is shaped by temporal usage patterns as well as changing relationships among weather conditions, operational factors, and historical charging behavior. Many existing forecasting models treat these explanatory variables mainly as parallel inputs, while their mutual relationships are often predefined, simplified, or left implicit in the temporal learning process. To support AI-driven charging demand management, this study proposes an adaptive graph-based recurrent network (A-GRN) for city-level aggregated EV charging-load forecasting. In the proposed framework, key explanatory variables are represented as feature nodes, and their connections are learned through an adaptive adjacency matrix rather than a fixed spatial topology. The adaptive graph neural network (AGN) module captures feature-level interactions, while a dual-path gated recurrent unit module (DG-GRU) extracts temporal representations from the charging-load sequence. Experiments on a city-level EV charging dataset show that A-GRN outperforms several baseline models, including naive persistence forecasting, GRU, LSTM, BiGRU, TCN, and GCN. Compared with the BiGRU baseline, A-GRN reduces MAE, MSE, and RMSE by 31.36%, 34.65%, and 20.48%, respectively. In the original physical unit, the MAE is reduced from 187.43 kWh to 128.64 kWh, and the RMSE is reduced from 222.69 kWh to 177.08 kWh. The results indicate that feature-level graph learning can improve short-term EV charging-load forecasting, especially when the target is an aggregated urban load rather than the load of a single charging station. The proposed model provides a data-driven forecasting tool for sustainable urban charging demand management, low-carbon transport operation, and charging infrastructure planning. Full article

To address the application requirements of dynamic simulation for sudden deep pavement potholes, this study presents an automated cover plate control device that integrates concealment, rapid response, and high load-bearing capacity, thereby overcoming the inherent contradiction between “portable yet weakly load-bearing” and “highly load-bearing yet inflexible” that has long limited conventional cover plate solutions. The core of the device comprises a cover plate mechanism consisting of a UHPC–Q235 composite cover plate, a distributed truss, and specially configured connecting rods, together with a winch hoisting mechanism, a hydraulic locking and rapid-release mechanism, and an embedded steel frame structure. Together, these modules realize a complete operational cycle of “closed load-bearing support → hydraulic release → gravity-driven rotation → winch reset.” Theoretical analysis and experimental measurements demonstrate that hydraulic release can be accomplished within 0.5 s, the cover plate can form a standard collapse pothole of 2000 mm in diameter within approximately 1 s, and a single cycle requires approximately 11 s, thereby faithfully reproducing the dynamic process of sudden pavement collapse. Refined mechanical design and ABAQUS finite element simulations verify that under the most adverse loading conditions, the stress in all structural components remains below the material design strength limit, with clear and reliable load transfer paths maintained in all operational states. The integrated camouflage design achieves over 95% visual and tactile similarity to the existing pavement surface, meeting the design requirement of concealment under normal conditions. The proposed device offers a high-fidelity physical simulation solution for autonomous vehicle perceptual training under emergent road hazards and for roadway safety assessment. Full article

Accurate vehicle state estimation is a critical prerequisite for electric vehicle motion control, yet its performance is highly sensitive to deviations in inertial parameters. Variations in vehicle mass and moment of inertia caused by changing loads can lead to model mismatch, thereby degrading the accuracy and robustness of state estimation. To this end, this paper proposes a hierarchical collaborative estimation framework that integrates the Maximum Correntropy Adaptive Unscented Kalman Filter (MCAUKF) with a Physics-Informed Neural Network (PINN) for inertial parameter identification and key state estimation in electric vehicles. The upper layer employs MCAUKF for robust online identification of unknown inertial parameters, such as vehicle mass and moment of inertia. The lower layer develops a PINN-based state estimator that incorporates physical constraints by embedding the coupled dynamic residuals of longitudinal, lateral, and roll motions into the supervised learning process, thereby enabling high-precision real-time estimation of key dynamic states, including yaw angle, longitudinal velocity, and roll angle. Simulation results demonstrate that the proposed method can effectively achieve coordinated estimation of inertial parameters and key states under varying load conditions and complex maneuvering scenarios, significantly improving overall estimation accuracy and robustness. Full article

Unsaturated polyester (UP) composites are widely utilized in engineering applications, including vehicle body structures, due to their ease of processing and good interfacial compatibility with natural fibers. However, the inherent brittleness of UP limits its performance under impact or tensile loading, as it exhibits minimal plastic deformation and is prone to crack initiation and propagation. In this study, bamboo fiber was incorporated into the UP matrix at various mixing ratios to enhance its crack resistance. After achieving uniform dispersion, the composites were subjected to a splitting tensile test to evaluate their crack resistance behavior. The results indicate that the composite containing 80% polyester exhibits the highest fracture toughness, with a crack resistance value of K_1C = 1.396 MPa·m^0.5. This value represents a 192.03% improvement compared with neat polyester (K_1C = 0.713 MPa·m^0.5). The enhanced crack resistance is attributed to the fiber bridging and energy-absorption mechanisms introduced by the bamboo fibers. These findings demonstrate the effectiveness of bamboo fiber reinforcement in improving the fracture performance of UP-based composites, highlighting their potential for use in lightweight structural applications. Full article

To address the challenges of identifying electric vehicles (EVs) in user-side scenarios where multi-source load data is coupled with high-penetration distributed photovoltaics (PV), we propose a robust EV identification framework based on net load decomposition and a two-stage decision-making process. Initially, a context-aware source-supervised separation (CSSS) algorithm is employed to decouple PV output from the net load, effectively eliminating PV-induced interference by constructing targeted feature vectors. Subsequently, four key features characterizing EV charging behavior are extracted to feed into a hierarchical identification model. The first stage utilizes a Composite Charging Characteristic Index (CCCI) for rapid preliminary screening, while the second stage implements sample-adaptive weighted stacking ensemble learning for high-precision detection. Experimental results demonstrate that the proposed method achieves an identification accuracy of 96.33%, with the load decomposition stage contributing a 1.2% improvement. This framework provides a reliable technical foundation for load analysis and demand-side management in distribution networks with high PV integration. Full article

Liquid-cooled battery thermal management systems are essential for maintaining thermal safety, temperature uniformity, and hydraulic efficiency in electric vehicle battery modules. However, improving heat dissipation often increases pressure drop and pumping demand, making the thermal–hydraulic trade-off a key challenge in cooling plate design. This study develops a CFD–GPR–NSGA-II-based multi-objective optimization framework for a mini-channel liquid cooling plate applied to a cylindrical 18650 lithium-ion battery module under a 4C discharge condition. The mini-channel thickness, wall thickness, and coolant inlet velocity are selected as design variables, while the maximum battery temperature, temperature difference, and pressure drop are used as objective functions. Sixty design samples are generated using Latin hypercube sampling and evaluated through CFD simulations. Gaussian process regression models are then constructed to approximate the nonlinear relationships between the design variables and the thermal–hydraulic responses, and the trained surrogate models are coupled with NSGA-II to identify Pareto-optimal solutions. The selected compromise design is finally verified using a full CFD simulation. Compared with the initial configuration, the CFD-verified optimized design reduces the maximum temperature, temperature difference, and pressure drop by 0.569 K, 0.557 K, and 338.612 Pa, respectively. Although the reduction in peak temperature is moderate, the optimized design improves temperature uniformity by 10.06% and reduces pressure drop by 43.25%, demonstrating a balanced improvement in thermal and hydraulic performance. A heat-load robustness check further confirms that the optimized design maintains a predictable thermal response under different heat generation levels. These results indicate that the proposed CFD–GPR–NSGA-II framework provides an effective and computationally efficient approach for designing mini-channel liquid cooling plates for electric vehicle battery thermal management. Full article

Heavy-haul railways require efficient rail replacement because extreme axle loads and high-density transport accelerate rail wear. Traditional manual-led processes are limited by fragmented operations, high labor demand, and complex equipment scheduling, typically completing about 1 km of rail replacement within a 4 h maintenance window and requiring approximately 340 workers. This study is positioned as construction-process modeling, workflow organization, and simulation-supported feasibility analysis for an integrated rail-changing workflow, rather than the development or field validation of a fully mature rail-changing machine. The proposed workflow coordinates rail unloading, on-board welding, fastener disassembly, rail cutting, exchange-recovery, fastening, closure welding, and final inspection through a highly parallel construction organization. A process-level train-set configuration, including a tractor, a long-rail comprehensive transport vehicle, an exchange-recovery integrated transport vehicle, and a mobile welding vehicle, is used as an engineering carrier to support the closed-loop workflow of unloading, welding, exchange, and recovery. Based on engineering time-study analysis, field experience, expert consultation, and discrete-event simulation, the results indicate that the proposed workflow has the potential to complete a simulated 2 km rail-changing task within a single 4 h maintenance window with an estimated labor demand of 80–95 personnel under the specified assumptions. The study provides conceptual and simulation-supported feasibility evidence for construction-process organization, rather than field-validated machine performance, and offers a technical reference for improving the mechanization and coordination of heavy-haul railway maintenance. Full article

Global climate change and extreme precipitation events increasingly challenge urban infrastructure resilience, particularly in topographically vulnerable regions like Taiwan. Traditional flood monitoring relies heavily on the manual visual interpretation of extensive surveillance networks, a process that imposes high cognitive loads and risks delayed emergency responses. This study presents a comprehensive Cyber-Physical System (CPS) architecture for an automated Water Image Monitoring Platform. Integrating approximately 10,000 cameras and multi-modal data—including precipitation records and spatial alerts—the platform leverages advanced semantic segmentation (DeepLabV3+ with Xception71) to delineate inundation boundaries. To ensure robustness under adverse conditions such as low illumination, fog, and specular glare, we implemented targeted optimizations, including HSV pre-processing, Deblur GAN architectures, and attention mechanisms. Results demonstrate a significant performance evolution, with the event recall rate rising from 88% in 2022 to 99.7% by 2025. A key driver of this success is the synergy between stationary nodes and vehicle-mounted CCTV units, which provide critical dynamic geographic coverage. Furthermore, the deployment of edge computing reduced warning latency 10 times—from 19.2 to 2 s—while virtual water level gauges maintained a mean error within ±10 cm. Despite these gains, a Human-in-the-Loop (HITL) architecture remains strategically necessary for ethical accountability and error filtering. This CPS provides a foundational model for autonomous, resilient urban disaster management. Full article

Unmanned aerial vehicle (UAV) swarms generate massive multi-modal sensor data streams from onboard payloads such as RGB cameras, LiDAR, and thermal sensors. Large language models (LLMs) can interpret these data for natural language-based swarm coordination. However, deploying LLMs directly on resource-constrained UAV nodes faces a critical bottleneck. Long-context textual sensor logs (e.g., continuous status reports with GPS, altitude, and detection events) lead to high prefill latency. Existing distributed inference frameworks suffer from load imbalance and pipeline bubbles, violating real-time mission requirements. To address these issues, we propose Orion, an edge-only collaborative inference framework for LLM-based sensor data processing in heterogeneous UAV swarms. Orion incorporates three innovations: (1) optimal model partitioning via dynamic programming, (2) adaptive sequence partitioning that balances causal attention load across pipeline stages, and (3) a predictive decoding mechanism that speculatively generates the first token during idle intervals. Experiments on a comprehensive simulation framework ((using Meta’s Llama-2 (Large Language Model Meta AI)) 7B/13B/70B and simulated UAV swarm sensor traces) show that Orion reduces prefill latency by 81% (7B) and 78% (13B) compared to the best cloud–UAV baseline. Among the evaluated frameworks, Orion is the only framework capable of running the full 70B model on memory-constrained UAV nodes, enabling real-time sensor-aware LLM inference. Full article

The low-cycle fatigue failure of drive shafts under complex service conditions constitutes a critical issue that undermines the structural integrity and service safety of the transmission system in special vehicles. To improve the prediction accuracy of the low-cycle fatigue life of drive shafts, a low-cycle fatigue life prediction method for the drive shaft that accounts for load effects and strength degradation is proposed. A fatigue life prediction model that accounts for the mean stress effect and fatigue strength degradation is proposed by introducing dynamically degrading fatigue strength into the mean stress-refined SWT (Smith–Watson–Topper) model. A fatigue cumulative damage model that considers load interactions and fatigue strength degradation is also proposed, in which the load ratio is introduced to quantitatively describe the extent of the influence of load interactions on the damage process. Furthermore, the dynamically degrading fatigue strength is incorporated into the M-H (Manson–Halford) model. Finally, the stress–strain responses at the critical locations of the drive shaft are analyzed using the finite element model, and the fatigue life of the drive shaft under the load spectrum is calculated using the improved fatigue life prediction model and the improved fatigue cumulative damage model. The results indicate that the improved life prediction method, which considers load effects and strength degradation, can effectively enhance the accuracy of fatigue life prediction for the drive shaft. Full article

Accurate battery state-of-charge (SOC) estimation is a core function of battery management systems (BMSs) for electric vehicles (EVs), as it directly affects energy management, safety, and reliability. However, battery aging significantly degrades the accuracy of conventional SOC estimation methods by causing capacity loss, increased internal resistance, and changes in voltage response characteristics. To address these issues, this study proposes an aging-aware SOC estimation method that combines an equivalent-circuit model (ECM) with an extended Kalman filter (EKF). In the proposed framework, aging effects are explicitly incorporated by using offline-identified SOH-dependent model parameters, including effective capacity, RC parameters, and SOC–OCV characteristics, and scheduling these parameters within the EKF prediction and correction process according to the available SOH information. Furthermore, the performance of the proposed method is experimentally validated under an Urban Dynamometer Driving Schedule (UDDS) using cylindrical lithium-ion cells with large current fluctuations. The experimental results demonstrate that the proposed aging-aware EKF maintains stable SOC estimation performance not only in the initial battery state but also throughout the gradual aging process and up to the end of battery life. These results demonstrate the potential of SOH-scheduled, aging-aware EKF-based SOC estimation to improve SOC accuracy in aged batteries under the investigated laboratory and dynamic load conditions. Full article

The joint optimization of routing and three-dimensional loading is a highly complex NP-hard combinatorial problem, particularly when stringent center-of-gravity (CoG) stability constraints are required for unmanned aerial vehicle (UAV) operations. Existing algorithms typically adopt a route-first, load-second evaluation strategy for these interconnected components, often yielding distance-optimal yet physically infeasible solutions. To address this bottleneck, this paper formulates the Three-Dimensional Loading-Constrained UAV Routing Problem (3DLC-UAVRP), integrating unloading sequence consistency, spatial packing feasibility, and CoG deviation control into the routing decision process. A guided collaborative optimization framework, GLS-WSCPA, is proposed, coupling an Improved White Shark Optimization (IWSO) algorithm for global route exploration with a Human-like Divide-and-Conquer Packing Strategy (HLDCPS) for spatial arrangement. Unlike conventional decoupled approaches that treat loading feasibility as a post hoc filter, a Center-of-Gravity-Guided Path Adjustment (CGPA) and Local Loading Repair (LLR) mechanism is introduced to establish a dynamic feedback loop between routing search and loading evaluation, so that CoG violations are actively translated into guided routing perturbations rather than simply triggering solution rejection. Experimental results demonstrate that GLS-WSCPA generally achieves better solutions than the compared algorithms across the tested problem scales, with the performance gap tending to widen as the instance size increases within the tested range. Ablation studies verify the complementary roles of CGPA and LLR, and sensitivity analysis confirms that moderately relaxing payload and CoG constraints reduces routing distance within safety boundaries. Case analysis shows that the proposed method reduces fleet size by 20% and total delivery distance by 6.85% compared to traditional decoupled strategies. Full article

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, centralized Intrusion Detection Systems (IDSs) face constraints related to latency, privacy exposure, and bandwidth overhead. These limitations motivate a transition to edge-enabled IoV architectures, where localized vehicular and anchor nodes supported by edge servers enable decentralized processing, enhanced privacy, and reduced communication load. To address these operational challenges, this paper proposes FedX (Federated Explainable Ensemble Intrusion Detection System), a privacy-preserving and explainable federated ensemble IDS that integrates XGBoost and LightGBM models across resource-constrained edge vehicles and roadside units (RSUs) to enable collaborative, low-latency anomaly detection without sharing raw data. By applying adaptive weighting based on model confidence and resource availability, FedX enhances robustness and efficiency while enabling explainable decisions via SHAP and LIME analysis, which highlights reliance on key features (flow duration, speed, RPM) for high-confidence (>97%) intrusion alerts grounded in domain-specific behavior. Privacy is further enforced through Gaussian differential privacy and secure aggregation to mitigate inference and inversion attacks. Experiments on the CICIoV2024 dataset show that FedX achieves 99.1% accuracy, outperforming existing federated ensemble IDS models by up to 2.1%. The system reduces communication overhead by 17% relative to full synchronization through adaptive weighted transmission and secure aggregation. It maintains negligible accuracy loss (<1.5%) under a strong privacy budget ($ϵ$ = 1.1). The deployment of proposed IDS on Raspberry Pi 4 underscores its efficacy for edge computing. Experimental results indicate that adaptive weighting yields a 1.8% performance increase, while resource profiling shows 45% lower CPU utilization and over 50% lower power consumption compared with centralized baselines. The findings demonstrate that FedX, combined with explainable AI enables trustworthy, interpretable, and energy-efficient intrusion detection for secure next-generation Edge-enabled IoV networks. Full article