Search Results (361)

Renewable Energy Communities (RECs) are a cornerstone of the European Union’s energy transition strategy, promoting decentralized and participatory energy models. A fundamental design aspect of RECs is the choice of Keys of Repartition (KoRs), which govern the allocation of locally generated energy among participants. This study evaluated the economic and technical impacts of four KoR strategies—static, dynamic (based on load or production), and hybrid—within the Portuguese regulatory framework. A simulation-based methodology was employed, considering both small and large-scale communities, with and without energy storage systems, including stationary batteries and electric vehicles (EVs). Results show that storage integration markedly improves self-sufficiency and self-consumption, with stationary batteries playing the most significant role, while EVs provided only a residual contribution. Furthermore, the results demonstrated that the choice of KoR has a decisive impact on REC performance: in small-scale communities, outcomes depend strongly on participant demand profiles and storage availability, whereas in large-scale communities, operational rules become the key factor in ensuring efficient energy sharing, higher self-consumption, and improved balance between generation and demand. Full article

The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article

Against the backdrop of rapid advances in computing, industry, and electric vehicles, DC–DC buck converters—as core point-of-load regulators—are critical for power supplies in applications with stringent voltage-stability requirements. This paper proposes a two-phase interleaved Buck converter based on positively coupled inductor with a high coupling coefficient. The innovation lies in the positively coupled inductor and two-phase interleaved architecture, where two MOSFETs and two diodes form a similar symmetrical full-bridge interleaved structures together achieve a higher conversion ratio and provide $ZCS$ operation for all power devices, thereby effectively reducing switching losses. Relative to traditional topologies, the proposed converter delivers a higher conversion ratio without extreme duty-cycle operation while improving reliability. After detailing the operating mechanism, we derive the input–output voltage relation, outline controller synthesis guidelines, and specify the soft-switching conditions. From the viewpoint of symmetry, the proposed interleaved converter exploits the electrical and magnetic symmetry between phases to achieve current balancing, extended duty-cycle range and soft-switching. Validation is provided by both a $PSIM$ simulation model and a $270W$ hardware prototype using an $STM32F103ZET6$, which achieves 93.3% peak efficiency at a conversion ratio of 0.45, demonstrating the practicality and effectiveness of the approach. 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 paper presents the model, methodology and results of experimental research focused on identification of the wave form generated during the crossing of 30-ton and 60-ton vehicles on a ribbon-type pontoon bridge and the analysis of its influence on the characteristics of the maximum draught. A review of the literature revealed that ribbon-type pontoon bridges are subject to significant vertical deflection. This results from the need to generate sufficient buoyant force to balance the weight of crossing vehicles. The area of maximum draught occurs directly beneath the vehicle and moves along with it, generating a front wave—referred to as a bow wave—which propagates along the crossing and alters the local draught of individual pontoons. Due to the fact that pontoon bridges transfer loads through buoyancy force, a key issue in the process of their design is the precise knowledge of the formation of the volume of the droughted part. No information was found in any publication about the influence of the front wave on the draught form of a ribbon-type pontoon bridge. Their authors do not indicate that the analytical or simulation models they use reflect this phenomenon. Equally, the analysis of the methodologies and results of experimental studies in this area did not show that any attempts were made to identify the form of the front wave. The paper presents the results of measurements of vertical displacements of individual pontoon blocks of the crossing and the characteristics of the front wave occurring during the passing of 30- and 60-ton vehicles with speeds ranging from 7.4 to 30 km/h. Based on the obtained data, an attempt was made to identify the phenomenon of undulation of the surface of the water obstacle and its impact on the loads on the bridge structure. The results allow for identifying a significant front wave with a wavelength of 30–50 m, appearing clearly at speeds above 21 km/h. This wave substantially affects the draught measurement—at a speed of 25 km/h, the maximum draught increased by approximately 30%. Statistical analysis confirmed the significance of this effect (p < 0.05), indicating that wave formation must be considered for accurate determination of pontoon block draught. Furthermore, the mass of the vehicle had a strong influence on the wave and draught parameters—the 60-ton vehicle produced wave troughs and draught depths 55–65% greater than those of the 30-ton vehicle. Full article

One of the bottlenecks hindering the applications (e.g., vehicle navigation) of blockchain–UCAN is privacy. A sharded blockchain can protect vehicle data to a certain extent. However, unbalanced shard loads lead to low throughput and poor feature extraction in blockchain–UCAN. This paper proposes an optimal image binarization method (OTSU-GK) to enhance image features and reduce the amount of uploaded data, thereby improving throughput. Specifically, OTSU-GK uses a Gaussian kernel method where the parameters are optimized using grid search to improve the calculation of the threshold. Additionally, we have designed a Node Load Score (NLS)-based sharding blockchain, which considers the number of historical transactions, transaction types, transaction frequency, and other metrics to balance the sharding loads and further improve throughput. The experimental results show that OTSU-GK improves by 74.3%, 58.7%, and 83% in SSIM, RMSE/MAE/AER, and throughput. In addition, it reduces IL by 70.3% compared to other methods. Full article

In the context of the rapid development of the new energy vehicle industry, how to achieve the mixed production of fuel vehicles and electric vehicles has become an important issue for the transformation and flexible manufacturing of automotive production lines. This paper addresses the balance problem of the mixed assembly line for electric vehicles and fuel vehicles and proposes a mathematical modeling method based on the product structure differences and workstation sharing. An improved genetic algorithm is designed for optimization. The established optimization model includes mathematical models of process priority relationships, cycle time constraints, synchronization constraints, and exclusive process co-placement constraints, with the optimization goals of minimizing workstation quantity and balancing workstation load. To solve such models, the decoding process of the genetic algorithm is redesigned in the algorithm design. The improved genetic algorithm can be well used to solve the workstation-sharing model. A case study of the chassis assembly line of an automotive manufacturing enterprise is used for verification. The results show that the method considering workstation sharing can effectively reduce the number of workstations, improve the distribution of workstation loads, and increase the utilization rate of the production line, while ensuring the cycle time constraints. The conclusions of this study expand the theoretical framework of the balance problem of mixed assembly lines and provide practical references for the transformation of fuel vehicle production lines into new energy vehicles. Full article

The controller of the energy management system must be capable of meeting the rapid and dynamic demands of fuel cell electric vehicles (FCEVs) without compromising its performance and durability. The performance of FCEVs can be enhanced through powertrain hybridization with battery and ultracapacitor systems. The overall dynamic optimization of the energy between the batteries/ultracapacitors and the Proton Exchange Membrane Fuel Cell (PEMFC) output can play an important role in hydrogen fuel economy and the durability of vehicle systems. The present study investigates the system’s efficiency and fuel consumption in European Drive Cycles when employing diverse energy management strategies. This investigation utilizes a novel switch real-time strategy (SWA_RTO), which is founded on an A-factor algorithm that alternates between the most effective Real Time Optimization (RTO) strategies. The objective of this paper is to underscore the significance of algorithmic optimization by presenting the optimal results obtained for the fuel economy of the SWA_RTO strategy. These results are compared with the basic RTO strategy and the static Feed-Forward (sFF) reference strategy. The load demand during driving cycles is primarily determined by the PEMFC system. Minor discrepancies in power balance are addressed by the hybrid battery and ultracapacitor system. Consequently, the lifespan of the subject will increase, and the state of charge (SOC) will no longer be a factor in monitoring. Full article

In autonomous operations of unmanned aerial vehicles (UAVs), accurate pose estimation is a core prerequisite for achieving autonomous navigation, obstacle avoidance, and task execution. To address the challenge of localization in GNSS-denied environments, Visual–Inertial Odometry (VIO) has emerged as a mainstream solution due to its outstanding performance. However, existing deep learning-based VIO methods exhibit limitations in their multi-modal fusion mechanisms. These methods typically employ simple concatenation or attention mechanisms for feature fusion. Furthermore, enhancements in accuracy are often accompanied by significant computational overhead. This makes it difficult for models to effectively handle complex, dynamic scenes while remaining lightweight. To this end, this paper proposes TSFNet (Two-stage Sequential Fusion Network), an efficient two-stage sequential fusion network. In the first stage, the network employs a lightweight visual backbone and a bidirectional recurrent network in parallel to extract spatial and motion features, respectively. A gated fusion unit is employed to achieve adaptive intra-frame feature fusion, dynamically balancing the contributions of different modalities. In the second stage, the fused features are organized into sequences and fed into a dedicated temporal network to explicitly model inter-frame motion dynamics. This decoupled fusion architecture significantly enhances the model’s representational capacity. Experimental results demonstrate that TSFNet achieves superior performance on both the EuRoC and Zurich Urban MAV datasets. Notably, on the Zurich Urban MAV dataset, it reduces the localization Root Mean Square Error (RMSE) by 62% compared to the baseline model, while simultaneously reducing the number of parameters and computational load by 76.65% and 24.30%, respectively. This research confirms that the decoupled two-stage fusion strategy is an effective approach for realizing high-precision, lightweight VIO systems. Full article

Long-endurance hypersonic vehicles face the dual challenge of withstanding extreme aerodynamic heating while meeting onboard power requirements. Integrating thermoelectric generators within thermal protection systems offers a solution by converting thermal loads into electrical power. However, accurate prediction requires resolving coupled multiphysics, where three-dimensional simulations are computationally prohibitive and existing one-dimensional models lack accuracy. This study develops a quasi-two-dimensional distributed thermal network incorporating shape-factor corrections for rapid, high-fidelity prediction. Multi-objective optimization is performed to balance specific power, thermal expansion mismatch, and thermal margin. Analysis reveals fundamental trade-offs: a maximum-power design achieves 28.1 W/kg but only a 0.8% thermal margin, whereas a balanced design delivers 24.5 W/kg with a 5.1% thermal margin and significantly reduced thermal stress. Despite geometric variations, peak conversion efficiency converges to approximately 13%. This indicates that efficiency is primarily governed by material properties, while geometric optimization effectively tunes temperature and thermal strain distributions, providing guidelines for reliable system development. Full article

This study establishes an urban-scale integrated energy system model, innovatively incorporating carbon emission cost constraints. Utilizing a source-load-storage collaborative planning approach, the research quantitatively evaluates the interplay between hydrogen energy penetration and carbon pricing policies on system evolution. A dynamic optimization algorithm is employed to identify the cost-minimal development pathway. Results reveal that a 30% increase in photovoltaic capacity and a 50% expansion in biomass power generation reduce annual system carbon emission intensity by 1.3 basis points, highlighting the decarbonization potential of renewable energy scaling. Hydrogen-based transportation substitution for fuel vehicles contributes 2.7% of the system’s total CO₂ reduction through full lifecycle emission savings. At a carbon price of 140 yuan/ton CO₂, market-driven energy structure optimization enhances renewable utilization by 11.2 percentage points, achieving a 0.3% annual reduction in societal emissions (equating to 297,000 tons). However, this scenario induces a 5% rise in end-user energy costs (approximately 530,000 yuan annually), underscoring the critical balance between decarbonization and economic viability. The study demonstrates that urban energy system planning must integrate dual objectives of carbon constraints and cost efficiency. Policy incentives are coupled with technological cost reductions in environmental and economic performance. This study provides quantitative evidence to guide low-carbon transition strategies for municipal energy 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

In the Internet of Vehicles (IoV), vehicles need to process a large amount of perception data to support tasks such as road navigation and autonomous driving. However, their computational resources are limited. Therefore, it is necessary to explore the combination of vehicle–road cooperation with edge computing. Roadside units (RSUs) can provide data access services for vehicles, and deploying edge servers on RSUs can improve the data processing capability in IoV environments and ensure the sustainability of vehicle communications, thus supporting complex traffic scenarios more effectively. In this work, we study the deployment of RSUs in vehicle–road cooperative systems. To balance the deployment cost of RSUs and the quality of service (QoS) of vehicle users, we propose an RSU deployment optimization model with six objectives, including time delay, energy consumption and security when vehicles offload their tasks to RSUs, as well as load balancing and the number and communication coverage area of RSUs. In addition, we propose a Wasserstein generative adversarial network (WGAN)-based Two_Arch2 (WGTwo_Arch2) to solve this many-objective optimization problem to better ensure the diversity and convergence of the solutions. In addition, a polynomial variation strategy based on Lecy’s flight mechanism and a diversity archive selection strategy with an adaptive Lp-norm are also proposed to balance the exploratory and exploitative capabilities of the algorithm. The effectiveness of the proposed algorithm WGTwo_Arch2 for 6-objective RSU deployment optimization is verified by comparisons with five different algorithms. Full article

Under China’s dual carbon goals, the surging summer demand for air conditioning (AC) has widen the power grid’s peak-to-valley difference, posing challenges to conventional supply-side solutions. To address this issue, effective demand-side coordination is essential. However, existing demand response schemes typically optimize single resources in isolation and overlook the dynamic evolution of user participation. This study proposes a price-guided coordination mechanism integrating vehicle-to-grid (V2G) and AC loads to establish a closed-loop value chain among the grid, aggregators, and users. A bi-level optimization framework is developed to balance the interests of these stakeholders. The model incorporates a V2G discharging willingness component that considers user psychology and battery degradation, as well as an AC response model reflecting thermal comfort. Simulation results demonstrate that the proposed mechanism effectively mitigates peak loads and narrows the peak-to-valley difference while enhancing off-peak electricity consumption. It accommodates the spatiotemporal and user-type heterogeneity of response behaviors, yielding notable economic gains for all participants. This research validates a comprehensive strategy for improving grid flexibility, protecting stakeholder interests, and optimizing user engagement, offering both theoretical insight and practical guidance for diversified resource integration. Full article

This study introduces a health-aware fuzzy logic (FL) energy management strategy (EMS) for fuel cell electric commercial vehicles (FCECVs) that aimed to improve energy efficiency and extending fuel cell system (FCS) lifespan. The FL-based EMS was developed using vehicle power demand and battery state of charge (SOC) as inputs, with the FCS power change rate as the output, aiming to mitigate degradation induced by abrupt load transitions. A multi-objective optimization framework was established to optimize the fuzzy logic controller (FLC) parameters, achieving a balanced trade-off between fuel economy and FCS longevity. The non-dominated sorting genetic algorithm-II (NSGA-II) was utilized for optimization across various driving cycles, with average Pareto-optimal solutions employed for real-time application. Performance evaluation under standard and stochastic driving cycles benchmarked the proposed strategy against dynamic programming (DP), charge-depletion charge-sustaining (CD-CS), conventional FL strategies, and a non-optimized baseline. Results demonstrated an approximately $38\%$ reduction in hydrogen consumption (HC) relative to CD-CS and over $75\%$ improvement in degradation mitigation, with performance superior to that of DP. Although the strategy exhibits an average $17.39\%$ increase in computation time compared to CD-CS, the average single-step computation time is only $2.1$ ms, confirming its practical feasibility for real-time applications. Full article