Search Results (70,510)

Vertiports, as dedicated facilities for electric vertical takeoff and landing (eVTOL) aircraft, are essential to ensure the efficiency and sustainability of Urban Air Mobility (UAM). However, UAM infrastructure site selection has become increasingly complex due to limited land availability, complex spatial conditions, and the need to balance multiple objectives. Focusing on passenger-carrying UAM operations, this study proposes a systematic framework for vertiport site selection. First, key factors are classified into high, medium, and low levels across the safety, economic, and social dimensions, forming a modular evaluation system. A GIS-based spatial screening process is developed to identify potential vertiport locations. Subsequently, a variable representing the level of demand satisfaction is incorporated into a progressive coverage model specifically designed for vertiport site optimization. A hybrid algorithm is designed to solve the model. Using Shenzhen as a case study, the proposed approach is validated through real-world data. The results show that vertiport size and spatial requirements significantly influence the selection of suitable land types. High economic constraints may cause facility over-concentration, while setting standards aligned with regional functions better supports equitable access. Locating vertiports in high-demand areas enhances demand satisfaction levels, and both service capacity and range strongly influence overall system performance. These findings provide practical insights for future vertiport planning, promoting the efficient use of urban resources and supporting the successful implementation and sustainability of UAM. Full article

Three composite materials, made by inserting the same amount of BiFeO₃/Bi₂₅FeO₄₀ powders (each powder having a different concentration of the secondary phase, Bi₂₅FeO₄₀: 10%, 20%, and 30%) into a silicone rubber (SR) matrix, were investigated to understand their electrical properties. Electrical conductivity measurements of the composite samples were carried out over a frequency range from 0.5 kHz to 2 MHz. The resulting conductivity spectra revealed two distinct regions: a low-frequency plateau corresponding to DC conductivity and a high-frequency region where AC conductivity increases with frequency. Some key electrical parameters, such as DC conductivity and band gap energy, were calculated using these measurements. An increase in Bi₂₅FeO₄₀ concentration resulted in a rise in DC conductivity from 5.61 × 10⁻⁵ S/m to 7.67 × 10⁻⁵ S/m across the composite samples. To gain further insight into the mechanisms of charge transport, both Jonscher’s universal response and the correlated barrier hopping (CBH) model were applied. The polaron model was also used to calculate the energy barrier for electrical conduction, but for higher temperatures (where the samples exhibit conductor behavior). The last part of the study was an aging analysis that showed a degradation of the investigated sample, as reflected by a decline in their conductive properties over time. Having no endothermic or exothermic events in the DTA curves, it is clear that the observed variation in conductive properties is not related to phase transitions, but it can be attributed to microstructural mechanisms, such as defects, microcracks, or structural disorders. These results can help in designing composite materials with desirable conductive properties by optimizing their filler concentration and processing conditions. Full article

Digital transformation has brought unprecedented transformation and opportunities in manufacturing enterprises. Focusing on 65 listed companies in the electric vehicle sector as the research objects and drawing on the “Technology–Organization–Environment” (TOE) framework, this study selects three dimensions—technology, organization, and environment—and six antecedent conditions. Using fsQCA configurational analysis, this research explores diverse paths to improving corporate performance, identifying five pathways. Among these, digital transformation and operational efficiency consistently serve as pivotal bridging conditions across multiple configurations. Furthermore, when enterprises demonstrate strong capabilities in both the technological and organizational dimensions, other conditions tend to act as substitutes, interacting synergistically with these core strengths to enhance overall firm performance. This study organically combines the TOE framework and fsQCA, deepening the application of the TOE theory in the field of electric vehicle manufacturing enterprises. Additionally, based on the configurational paths derived from the research, it provides differentiated countermeasure suggestions for electric vehicle manufacturing enterprises, offering practical guidance for enhancing their performance in the context of digital transformation. Full article

Complex non-Newtonian glues are widely used in electrical vehicle (EV) manufacturing plants. In this paper, we focus on initial transient and compressibility issues which are closely associated with high pressure, boundary conditions, and flexible tubes, as well as their respective fluid–structure interaction effects. Both thixotropic and power law non-Newtonian nearly compressible fluid models have been employed to couple with flexible tubes with two different sets of material properties, namely, Young’s modulus and density. In addition to thick-wall cylindrical pressure vessel solutions, different pressure and velocity boundary conditions have also been studied with the consideration of initial transient and steady solutions for acoustic models. Moreover, the radial direction displacement distributions through the tube wall thickness and axial directions compare well within 4 to 9 percentage points with theoretical solutions of thick-wall cylinders under internal and external pressures. Finally, inverse optimization methods have been employed for the calibration of key parameters in comparison with experimental and computational results. Full article

As applications like electric vehicles, all-electric ships, and all-electric aircraft continue to evolve, Noise, Vibration, and Harshness (NVH) issues have garnered extensive attention. However, as the core of the power system, permanent magnet synchronous motors (PMSMs) still lack control algorithms that consider vibration problems. Therefore, this paper proposes a model-based predictive vibration suppression algorithm to suppress the PMSM vibration. Firstly, this paper explores the influence of armature currents on vibration by analyzing the vibration characteristics of PMSMs, and proposes a minimum vibration current model. On this basis, according to the torque conditions required for the stable operation of the motor, a model-based predictive vibration suppression algorithm is designed. Finally, the effectiveness of the proposed algorithm is verified through prototype experiments. Full article

To address the challenges of power generation rights trading and profit distribution in the integrated energy system of multi-park combined cooling, heating, and power (CCHP) with new energy grid integration, we constructed a hierarchical game model involving multi-energy system aggregators. By having aggregators price electricity, heat, cold, and carbon costs, the model establishes a hierarchical game framework with the linkage of the four prices (electricity, heat, cold, and carbon), achieving inter-park peer-to-peer (P2P) multi-energy dynamic price matching for the first time. It aims to coordinate distribution network dispatching, renewable energy, energy storage, gas turbine units, demand response, cooling–heating–power coupling, and inter-park P2P multi-energy interaction. With the goal of optimizing the profits of integrated energy aggregators, a hierarchical game mechanism is established, which integrates power generation rights trading models and incentive-based demand response. The upper layer of this mechanism is the profit function of integrated energy aggregators, while the lower layer is the cost function of park microgrid alliances. A hierarchical game mechanism with Two-Level Optimization, integrating the Adaptive Disturbance Quantum Particle Swarm Optimization (ADQPSO) algorithm and the branch and bound method (ADQPSO-Driven Branch and Bound Two-Level Optimization), is used to determine dynamic prices, thereby realizing dynamic matching of energy supply and demand and cross-park collaborative optimal allocation. Under the hierarchical game mechanism, the convergence speed of the ADQPSO-driven branch and bound method is 40% faster than that of traditional methods, and the optimization profit accuracy is improved by 1.59%. Moreover, compared with a single mechanism, the hierarchical game mechanism (Scenario 4) increases profits by 17.17%. This study provides technical support for the efficient operation of new energy grid integration and the achievement of “dual-carbon” goals. Full article

In this study, a multi-objective shape optimization framework was established for photovoltaic-powered underwater vehicles (PUVs) to systematically investigate multidisciplinary coupled design methodologies. Specifically, a global sensitivity analysis was conducted to identify four critical design parameters with 24 h energy consumption and cabin volume serving as dual optimization objectives. An integrated automated optimization workflow was constructed by incorporating parametric modeling, computational fluid dynamics (CFD) simulations, and dynamic surrogate models. Additionally, a new phased hybrid adaptive lower confidence bound (PHA-LCB) infill criterion was designed under the consideration of error-driven mechanisms, improvement feedback loops, and iterative attenuation factors to develop high-precision dynamic surrogate models. Coupled with the NSGA-II multi-objective genetic algorithm, this framework generated Pareto-optimal front solutions possessing significant engineering value. Furthermore, an optimal design configuration was ultimately determined through multi-criteria decision analysis. Compared to the initial form, it generates an additional 1148.12 Wh of electrical energy within 24 h, with an 22.36% increase in sailing range and a 2.77% improvement in cabin volume capacity. The proposed closed-loop “modeling–simulation–optimization” framework realized multi-objective optimization of PUV shape parameters, providing methodological paradigms and technical foundations for the engineering design of next-generation autonomous underwater vehicles. Full article

With the large-scale power batteries approaching their retirement phase, efforts are being made to advance the recycling and cascade utilization of power batteries for electric vehicles (EVs). This paper constructs a closed-loop supply chain (CLSC) of power batteries led by the battery manufacturer (BM) and composed of the electric vehicle manufacturer (EVM) and third-party recycler (TPR). The study investigates the optimal pricing strategies of this CLSC with the consideration of recycling competition under the government’s reward–penalty mechanism. This paper establishes five recycling modes, namely independent recycling and cooperative recycling, under dual-channel recycling, and further discusses the effects of the government reward–penalty mechanism and recycling competition on the recycling rate, profits, and recycling pricing of the CLSC in each recycling mode. The following conclusions are found: (1) An increase in the reward–penalty intensity will increase the recycling rate, sales price of EVs, wholesale price, transfer price, recycling price, and the profit of each recycler in the CLSC. (2) An increase in the recycling competition will result in the reduction of the profit of each enterprise, and will also lead to the reduction of the recycling rate. (3) Cooperation between enterprises can inhibit the recycling volume of other enterprises to a certain extent. The cooperation between the EVM and BM can increase the recycling volume and the sales volume of EVs. (4) The leadership of the BM in the supply chain is embodied in the recycling and profit. For other members of the supply chain, it is very important to strive for cooperation with the leaders in the supply chain. These research conclusions can provide theoretical support for optimizing the power battery recycling system, formulating relevant policies, and improving the efficiency of resource recycling, thereby promoting the sustainable development of the new energy industry. Full article

This work presents the use of a novel impedance coupling technique and electrical length increase by using stub loading placed from the radiator to the ground plane. This method is applied to the design of a small four-element ultrawideband (UWB) MIMO antenna arranged in axial symmetry to achieve a compact array size while obtaining a bandwidth starting from a very low cutoff frequency compared to a conventional radiator operating at the same frequency. The four-element MIMO antenna, with an operational bandwidth of 1.9 GHz to 30 GHz, is based on a wideband monopole with a semicircular geometry, fed by a coplanar structure and an L-shaped half-ground plane section. To increase the electrical length of the structure and achieve a compact antenna design, reactive stub loading is introduced, placing it on the backside of the substrate, located orthogonally between the radiator and the L-shaped ground plane, obtaining a small-sized configuration. The axial symmetry is employed to increase the antennas’ isolation by taking advantage of the orthogonal positioning and making the radiated fields have a low correlation. The antenna array footprint measures 48 mm × 48 mm, corresponding to 0.3λ₀ × 0.3λ₀ at the lower cutoff frequency. The array exhibits a low envelope correlation coefficient (ECC) of around 0.033 at 2 GHz, and less than 0.001 at the rest of the bandwidth; a diversity gain (DG) of approximately 10; a stable total active reflection coefficient (TARC) below −10 dB; interport isolation between 20 and 40 dB; and an average gain of 2.8 dBi. Full article

With the continued growth in wind power installed capacity and electricity generation, accurate wind power forecasting has become increasingly critical for power system stability and economic operations. Currently, short-term wind power forecasting often employs deep learning models following modal decomposition of wind power time series. However, the optimal length of the time series used for decomposition remains unclear. To address this issue, this paper proposes a short-term wind power forecasting method that integrates improved modal decomposition with deep learning techniques. First, the historical wind power series is segmented using the Pruned Exact Linear Time (PELT) method. Next, the segmented series is decomposed using an enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to extract multiple modal components. High-frequency oscillatory components are then further decomposed using Variational Mode Decomposition (VMD), and the resulting modes are clustered using the K-means algorithm. The reconstructed components are subsequently input into a Long Short-Term Memory (LSTM) network for prediction, and the final forecast is obtained by aggregating the outputs of the individual modes. The proposed method is validated using historical wind power data from a wind farm. Experimental results demonstrate that this approach enhances forecasting accuracy, supports grid power balance, and increases the economic benefits for wind farm operators in electricity markets. Full article

Energy management for hybrid electric commercial vehicles, involving continuous power output and discrete gear shifting, constitutes a typical mixed-integer programming (MIP) problem, presenting significant challenges for real-time performance and computational efficiency. To address this, this paper proposes a physics-informed neural network-optimized model predictive control (PINN-MPC) strategy. On one hand, this strategy simultaneously optimizes continuous and discrete states within the MPC framework to achieve the integrated objectives of minimizing fuel consumption, tracking speed, and managing battery state-of-charge (SOC). On the other hand, to overcome the prohibitively long solving time of the MIP-MPC, a physics-informed neural network (PINN) optimizer is designed. This optimizer employs the soft-argmax function to handle discrete gear variables and embeds system dynamics constraints using an augmented Lagrangian approach. Validated via hardware-in-the-loop (HIL) testing under two distinct real-world driving cycles, the results demonstrate that, compared to the open-source solver BONMIN, PINN-MPC significantly reduces computation time—dramatically decreasing the average solving time from approximately 10 s to about 5 ms—without sacrificing the combined vehicle dynamic and economic performance. Full article

Early warning signals of critical transitions in the piping process are essential for predicting dike hazards. This study proposes a new approach that combines Critical Slowing Down (CSD) theory with electrical signals analysis to identify precursor characteristics during the evolution of piping in a dual-layer dike foundation. A laboratory experiment was conducted to simulate the piping process while monitoring electrical signals in real-time. Ensemble Empirical Mode Decomposition (EEMD) was employed to analyze the time-series characteristics of the electrical signals from multiple perspectives. The results demonstrate that low-frequency components effectively track the gradual development of piping, while high-frequency components are sensitive to abrupt transitions near the critical point of failure. Statistical analysis reveals that the variance of the low-frequency components increases suddenly 5.09 min before the formation of the piping outlet and 5.53 min before piping occurs, providing a clear early warning capability. In contrast, the variance of the high-frequency components increases suddenly only 0.26 min and 0.45 min in advance, offering a short-term warning. These sudden increases serve as the effective precursory characteristics of critical transitions in the piping process. These findings confirm the presence of CSD characteristics in electrical signals and establish variance-based indicators as reliable precursors for different stages of piping evolution. The proposed methodology offers both theoretical insight and practical guidance for enhancing early warning strategies for dike failure. Full article

Composite materials have been increasingly used in various branches of industry, transport, construction, and medicine—as well as in other sectors of the economy and science—in recent decades. A significant advancement in the improvement of composite material characteristics has been achieved through the use of nanoparticles, which substantially enhance the properties of the base material, whether it is the matrix or the reinforcing phase in hybrid composites. The broad application of polymers and polymer composites in many areas of engineering has had a significant impact on reducing friction and wear, improving the thermal characteristics of individual components and entire technical systems, enhancing electrical conductivity, reducing the specific weight of components, lowering noise and vibration levels, and ultimately decreasing fuel consumption, production costs, and the costs of operation and maintenance of technical systems. This paper explores the potential applications of polymer composites in various assemblies and components of conventional vehicles, as well as in hybrid and electric vehicles. Furthermore, their use in medicine and the defense industry is examined—fields in which some authors believe composites were first pioneered. Finally, aviation represents an indispensable domain for the application of such materials, presenting unique exploitation boundary conditions, including dynamic environmental changes such as variations in temperature, pressure, velocity, and direction, as well as the need for high levels of protection. Future research can be unequivocally focused on the structural and technological advancement of polymer composites, specifically through optimization aimed at reducing waste and lowering production costs. Full article

This study presents the development and the mechanical and clinical characterization of a flexible biodegradable chitosan–glycerol–graphite composite strain sensor for real-time respiratory monitoring, where the main material, chitosan, is derived and extracted from Tenebrio Molitor larvae shells. Chitosan was extracted using a sustainable, low-impact protocol and processed into a stretchable and flexible film through glycerol plasticization and graphite integration, forming a conductive biocomposite. The sensor, fabricated in a straight-line geometry to ensure uniform strain distribution and signal stability, was evaluated for its mechanical and electrical performance under cyclic loading. Results demonstrate linearity, repeatability, and responsiveness to strain variations in the stain sensor during mechanical characterization and performance, ranging from 1 to 15%, with minimal hysteresis and fast recovery times. The device reliably captured respiratory cycles during normal breathing across three different areas of measurement: the sternum, lower ribs, and diaphragm. The strain sensor also identified distinct breathing patterns, including eupnea, tachypnea, bradypnea, apnea, and Kussmaul respiration, showing the capability to sense respiratory cycles during pathological situations. Compared to conventional monitoring systems, the sensor offers superior skin conformity, better adhesion, comfort, and improved signal quality without the need for invasive procedures or complex instrumentation. Its low-cost, biocompatible design holds strong potential for wearable healthcare applications, particularly in continuous respiratory tracking, sleep disorder diagnostics, and home-based patient monitoring. Future work will focus on wireless integration, environmental durability, and clinical validation. Full article

The increasing integration of intermittent renewable energy sources necessitates effective energy storage solutions, with battery energy storage systems (BESSs) emerging as promising candidates for energy arbitrage operations. This study conducted a comprehensive comparative analysis of 29 European electricity markets to identify optimal locations for utility-scale BESS-enabled energy arbitrage ventures. Using hourly wholesale electricity price data spanning January 2015 to December 2023, we employed statistical analysis techniques, 3D surface plots, and developed a novel energy arbitrage feasibility (EAF) score-based ranking system that integrates electricity market volatility metrics with regulatory and economic variables including gross domestic product per capita, index of economic freedom, and electricity supply-origin risk (ESOR). Five investor preference scenarios were analyzed: risk-averse, ESOR-sensitive, economy-sensitive, volatility-sensitive, and equally weighted approaches. Results demonstrated that Estonia ranked highest in three scenarios, achieving the maximum absolute EAF score of 0.558197 in the volatility-sensitive scenario, while Luxembourg led in the ESOR and economy-sensitive scenarios. Estonia’s market characteristics support single daily charge–discharge cycles, whereas Luxembourg enables dual cycles, offering different operational strategies. The EAF scoring methodology provides a standardized framework for cross-country investment decision-making in energy arbitrage ventures. These findings indicate that market selection significantly impacts the BESS arbitrage profitability, with Estonia and Luxembourg representing the most favorable investment destinations. Full article