Search Results (26,201)

The rapid increase of electric mobility is challenging the deployment design and operation of electric vehicle charging infrastructure in a scalable, sustainable, operationally reliable, and regulation-compliant manner. Although advances in both digitization and artificial intelligence in recent years have made smarter charging solutions possible, today’s approaches tend to concentrate on individual technical parts without considering holistic views. This paper introduces an AI-driven integrated management model for sustainable EV charging infrastructures, composed of four interconnected layers, namely, Eco-Design, Digital Tools, Risk Management, and Governance. In particular, each layer focuses on specific aspects of functionality, including environmentally friendly design decisions, digital monitoring capabilities, proactive risk reduction, and strategic coordination. Compared with existing approaches that address isolated technical or operational aspects, the proposed model provides an integrated, multi-layer architecture that unifies eco-design, digital intelligence, risk management and governance, offering a more holistic and scalable foundation for sustainable EV charging infrastructures. It represents the conceptual output of a structured integration of existing technologies, design principles and governance needs. Considering that fragmented, solution-specific advances are reduced by including interdependencies between layers, the model allows us to better integrate technical operations, resilience mechanisms and sustainability goals. The model is theoretical and offers a scalable point of reference for researchers, as well as infrastructure operators and politicians. Full article

Effective online bond wire degradation assessment of power modules is crucial for ensuring long-term stability. However, its electrical aging indicators are often influenced by junction temperature ($T_j$), and conventional $T_j$ monitoring methods are also affected by the aging process itself, creating a contradiction. This paper proposes a thermal–electrical co-modeling method designed to reduce reliance on accurate $T_j$. A major challenge of the method is the traditional thermal network models, which rely on case temperature ($T_c$). These models are affected by thermal coupling and have a slow dynamic response, making them difficult to integrate with electrical models. To overcome this, a $T_j$ monitoring method based on in situ sensor fabrication is employed to shorten thermal conduction path and simplify thermal network. This method results in a much faster dynamic process and is unaffected by thermal coupling, as confirmed through both theoretical analysis and finite element simulation. To validate the proposed method, bond wire degradation assessment is conducted using the on-state voltage drop ($V_{ce}$). Tested in practical circuits, this design successfully enables online evaluation of bond wire degradation, which is unaffected by $T_j$. Full article

Accurate photovoltaic power probability forecasting assists dispatch departments in making rational decisions. Joint probability distributions constructed using Copula functions can flexibly characterize complex nonlinear correlations and tail dependencies among random variables. However, existing research has not thoroughly explored the multivariate dynamic coupling characteristics related to forecasting errors, nor has it sufficiently considered the complementary advantages among different Copula functions. To address this, we propose a method for forecasting photovoltaic power output probabilities days in advance, integrating multivariate dynamic Copula functions with reinforcement learning. First, to capture the time-varying structure of photovoltaic power-related variables, we introduce a sliding time window for segmented modeling of historical data, fitting marginal probability distributions for predicted irradiance, forecasting power, and forecasting error. Second, a joint probability distribution of dynamic Gaussian Copula and t-Copula is constructed based on historical samples within the time window, generating a probabilistic prediction interval for the target time. Finally, reinforcement learning is employed to adaptively combine the probability prediction intervals derived from both Copula types, yielding the final photovoltaic power probability forecast. Simulations using actual operational data from a photovoltaic power plant in Shanxi Province validate the effectiveness of the proposed method. Full article

The push for higher energy density in electric vehicles has resulted in large-sized lithium-ion batteries, but their geometric upscaling exacts a heavy thermal price. Under high-rate discharge, these massive cells become heat traps, risking thermal runaway. To tame this instability, this paper engineered a hybrid management strategy fusing liquid cooling, Phase Change Materials (PCMs), and flow deflectors. With a primary focus on the structural optimization of the cooling channel, a three-dimensional numerical model, calibrated using experimentally determined thermophysical properties, was developed to overcome the thermal bottlenecks of conventional cooling architectures. Results indicated that the initial channel optimization effectively reduced the maximum temperature to 327.7 K, but it still remained near the safety threshold. Integrating PCM radically altered the thermal landscape, slashing the outlet temperature differential by 41.67% (from 2.76 K to 1.61 K) compared to pure liquid cooling and blunting peak thermal spikes. Furthermore, to overcome laminar stagnation, strategic deflector baffles were introduced to agitate the coolant, enhancing heat dissipation. Specifically, the optimal half-coverage (L = 1/2) baffle configuration successfully lowered the maximum temperature to 322.42 K while substantially reducing the system pressure drop from 948.16 Pa to 627.57 Pa, achieving a 33.33% reduction compared to the full-coverage scheme. Finally, a multi-variable sensitivity analysis confirmed the extraordinary engineering robustness of the optimized configuration, demonstrating a negligible maximum temperature fluctuation of less than 0.5% despite ±10% operational and material uncertainties. This synergistic system actively stabilizes the thermal envelope, offering a robust engineering blueprint for next-generation high-power battery packs. Full article

Accurate and physically consistent state prediction is essential for shipboard power systems (SPS) operating under dynamic conditions. However, purely data-driven models often exhibit degraded robustness and physically inconsistent outputs when exposed to transient disturbances or limited data coverage. To address these limitations, this paper proposes a physics-constrained hybrid prediction model that integrates a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture with wide residual connections (WRC) and a physics-constrained loss (PCL). The proposed modeling approach combines real operational measurement data with high-resolution simulation data to enhance data diversity and improve generalization capability. The CNN–BiLSTM structure captures nonlinear temporal dependencies, while the WRC preserves critical low-level transient electrical features during deep temporal modeling. In addition, multiple physical constraints, including power balance, voltage conversion relationships, and battery state-of-charge (SOC) dynamics, are incorporated into the training process to enforce physically consistent predictions. The model is validated using charging and discharging experiments on a laboratory-scale SPS under both steady-state and transient conditions. Comparative results demonstrate that the proposed approach achieves higher prediction accuracy, improved dynamic stability, and faster recovery following disturbances compared with conventional data-driven models. These results indicate that physics-constrained deep learning provides an effective and interpretable modeling framework for SPS state prediction, supporting digital twin-oriented monitoring and real-time prediction applications. Full article

Industrial heritage in resource-depleted mining towns faces the dual challenge of physical decay and social severance. To achieve sustainable urban revitalization, adaptive reuse strategies must align with local collective memory and emerging experiential consumption trends. Adopting a Scene Theory perspective, this study constructs a multi-level analytical framework using Meitanba Town (Hunan, China) and its power plant as a case study. A mixed-methods approach was employed, combining semantic network analysis of 1582 online user comments with 61 offline questionnaires distributed to local residents to quantitatively diagnose current scene elements, functions, and features. The quantitative results reveal a significant imbalance: while “Functional Media” achieved the highest comprehensive score (10.0) due to strong historical recognition, “Diverse Groups” scored the lowest (3.4), indicating a lack of social inclusivity. Specifically, residents expressed the highest demand for sports facilities (31.2%) and cultural spaces (23.7%), identifying the main workshop (26.4%) and chimney as core carriers of industrial identity. Responding to these findings, the paper proposes three targeted strategies: (1) Activate: creating open-access recreation scenes to satisfy urgent sports demands; (2) Link: constructing immersive cultural scenes to narrate the “coal–electricity–life” history; and (3) Enhance: developing industry-powered commercial scenes to avoid homogenization. This study enriches the localized application of Scene Theory and provides a data-driven, context-adjustable analytical and strategic model that can inform the sustainable renewal of mining towns globally, with its specific implementation requiring adaptation to local social, economic, and cultural characteristics. Full article

In order to provide an innovative form of urban air mobility, a new and versatile generation of small, highly automated aircraft is currently being developed. This is made feasible by the development of new technologies such as electrified powertrains, Vertical Take-Off and Landing capabilities and distributed propulsion systems. The operation of these novel aircraft types will generate a new source of air traffic noise. In particular, the perception of noise and the annoyance caused by these aircraft and their distributed propulsion systems are likely to deviate from those of conventional aircraft and will also depend on psychoacoustic effects. Thus, the noise emission and its subjective perception will be key factors for the success of urban air mobility vehicles and their acceptance by society. In order to investigate acoustic effects that enable low-noise aircraft design, a multidisciplinary approach is applied to develop new aircraft concepts for urban air mobility. This approach includes the conceptual design of two vehicles, one vehicle with tilt-rotors and one with tiltable, ducted fans; the sizing of an electric powertrain; the design and manufacturing of a wingtip rotor; and the design and manufacturing of the low-speed ducted fans. This paper presents the design of the two vehicle architectures, including their electric powertrain, as well as the aerodynamic and acoustic performance of the rotor and fan. Full article

This paper introduces a hybrid metaheuristic approach for the reconfiguration of electric distribution networks, integrating Simulated Annealing (SA) and Tabu Search (TS) to accelerate convergence and enhance exploration of the solution space. The method employs a selective mesh-based neighbor generation strategy, which substantially reduces the search space while maintaining operational feasibility (radial topology, voltage, and current limits). The approach was implemented in Python and integrated with DIgSILENT PowerFactory, enabling the direct evaluation of losses, voltages, and currents for reproducible and scalable analysis. Validation on 5-, 16- and 33-bus benchmark systems consistently reached the global optimum across 100 simulation runs, demonstrating robustness and computational efficiency. A real-world application was performed on the 10 kV primary distribution network of Huancayo, Peru, where the proposed method achieved a 10.4% reduction in active losses, improved the minimum voltage from 0.931 to 0.949 p.u., and partially relieved feeder overloads. These results confirm the method’s suitability for both academic benchmarking and practical deployment in Latin American distribution systems. Full article

During routine operation, the power batteries of electric buses (EBs) gradually age due to the combined effects of numerous factors, including charging/discharging cycles, load fluctuations, and ambient temperature. This paper focuses specifically on the problem of battery aging in the context of actual urban electric bus system operations. It explores how to comprehensively incorporate the battery degradation effect into optimization schemes for EB fleet scheduling. This paper proposes an integrated optimization methodology that combines a capacity degradation model, a scheduling optimization model, and a genetic algorithm. A comprehensive scheduling optimization model is constructed, incorporating vehicle procurement costs, operational charging costs, off-peak charging costs, and battery capacity degradation costs, subject to rigorously defined constraints. Subsequently, an improved genetic algorithm framework is developed. Finally, the constructed model is validated using operational data from the Chongqing bus system. An analysis of the optimization mechanisms is provided, and a sensitivity analysis is conducted on vehicle procurement costs and battery capacity degradation costs. Based on the results, the model can reduce the total cost to 92% of the original level, proving that it is effective to a certain extent in reducing the operating costs of electric buses. Full article

The present study examines the regulatory effects of laser heating parameters (power, position, and spot radius) on hydrothermal wave instability, heat and mass transfer, and interfacial deformation in bilayer thermocapillary systems under normal gravity. It provides theoretical support for the efficient utilization of energy and the optimization of industrial thermal systems, meeting the demands of sustainable development. The results show that increasing laser power induces asymmetric flow bifurcation nears the laser heating point, enhancing hydrothermal waves in the left region while suppressing them in the right region, with oscillation periods decreasing monotonically and amplitudes showing non-monotonic variation. Laser heating position alters convection intensity distribution, in which the convection in the hot zone is weakened as the laser point nears the cold end, while the convection in the cold zone is strengthened as the laser point nears the hot end. Reducing spot radius significantly decreases temperature gradients near the interfacial heat source, while attenuating horizontal velocity amplitude and increasing oscillation period, effectively suppressing oscillatory thermocapillary convection. This study demonstrates that precise control of laser heating parameters can effectively suppress thermocapillary instability and optimize heat transfer without introducing additional mechanical disturbances. It provides a theoretical basis for efficient, low-energy, non-contact thermal flow control technologies. Full article

Inspired by the growing demand for small and effective optoelectronic devices, this paper presents a simulation-based analysis of InGaN/GaN quantum dot light-emitting diode, focusing on the effects of systematic variation in both anode and cathode contact regions, as well as overall device size. Two-dimensional simulations using APSYS software were used to examine the impact of scaling the device dimensions as well as the individual contact dimensions on significant performance parameters like internal quantum efficiency (IQE), optical output power, and current-voltage (IV) response. We simulated five LED device sizes that is 50 × 50 µm², 100 × 100 µm², 200 × 200 µm², 300 × 300 µm², and 400 × 400 µm². As device size grows, so does the total current at each voltage. The highest current measurement is achieved by the device with dimensions 400 × 400 µm² while the lowest is observed on the device with dimensions 50 × 50 µm². In addition to changing the device dimensions, we ran extensive simulations on the sizes of p-type and n-type contacts. Notable changes were seen in the efficiency, optical power, and emission profile of the p-contact. The behavior of p-side contacts from 0 to 50 µm was the same, while contacts between 60 and 100 µm showed significant differences. The significant performance parameters were unaffected by changes to n-contact dimensions. The results of this study illustrate how the configuration of contacts and dimensions greatly influences the electrical and optical performance of quantum dot light-emitting diode. The results are believed to be helpful to researchers working on the design of next-generation compact and efficient solid-state lighting devices. Full article

Silicone gel packaging for high-voltage power devices suffers severe insulation degradation under complex conditions involving sustained high voltages and steep pulses. DC superimposed pulse electric fields are the primary cause. However, existing research lacks a systematic quantitative analysis of key influencing factors. Motivated by this inadequacy, this study quantified the effects of four core factors via control variable-based electrical tree experiments and revealed the microscopic mechanism through charge vibration experiments. Results indicate that pulse voltage slew rate is the most critical factor, as the impact of altering the pulse voltage slew rate on the parameters of the electrical tree exceeds 200%, while the impacts of altering the superimposed DC amplitude and duty cycle are 49.92% and 77.56%, respectively. Further discussion demonstrates that pulse voltage slew rate reflects the charge dynamic behaviors, while DC amplitude and duty cycle reflect charge static accumulation, with charge dynamic behaviors posing a more significant effect. This work clarifies key control parameters for silicone gel insulation degradation and the intrinsic influence chain from influencing factors to molecular stress, charge dynamic behaviors, electrical tree growth and silicone gel insulation degradation, providing theoretical support and technical guidance for optimizing the design and enhancing the reliability of silicone gel in power electronic devices packaging insulation. Full article

The harvesting of energy from movements is one of the purposes of triboelectric nanogenerators (TENGs). Among the various devices designed to perform this function, floors are one of the primary ones, as they do not need to be individually fitted to each subject and can be manufactured and installed on a large scale. This work classifies previously published TENG-based floors based on their materials, electrical performance in terms of the voltage, current, and power they produce, and their application in different fields. The materials used have been correlated with other important aspects for floors, such as weather or flame resistance, sustainability, recyclability or biodegradability of materials, and price. The synthesis of the variety of TENG-based floor models, which incorporate novel materials, hybrid technologies, or various functionalities, among other characteristics, can enrich and inspire the reader to enhance the performance of future floor designs based on the triboelectric effect. In addition, a novel triboelectric floor design made of nitrile butadiene rubber (NBR) and fluorine kautschuk material is presented, along with the electrical power generated when tribolayers are in contact. For the three floor strips measuring 40 cm long × 4 cm wide and 1 mm thick, electrical current and voltage output was measured, achieving nearly 0.1 W (20 V & 4.5 mA) of electrical power generation. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

Power-to-X technologies play a crucial role in accelerating the energy and material transition. A key opportunity lies in integrating these systems with existing bio-based infrastructures such as anaerobic digesters, providing a reliable source of biogenic carbon. Developing effective Power-to-Methanol (PtM) pathways requires a comprehensive understanding of process behavior through detailed simulation, including technical performance, economic feasibility, and environmental consequences. Despite growing interest, substantial variation remains in published levelized methanol costs, and many assessments insufficiently account for the full environmental footprint of production routes. This study evaluates the potential of PtM deployment in the Netherlands by comparing two pathways that utilize biogenic carbon sources: (i) hydrogenation of captured CO₂ using green hydrogen and (ii) dry methane reforming (DMR) of biogas, followed by catalytic syngas conversion to methanol. Results indicate that operational expenses—mainly driven by renewable electricity consumption—far outweigh capital investment. Both routes yield an LCoMeOH of approximately €2630 per tonne, about five times the cost of fossil-based methanol. Life cycle analysis shows that DMR performs more favorably overall, although elevated freshwater ecotoxicity and eutrophication result from digestate application as fertilizer. Continued improvements in renewable energy integration and nutrient recovery technologies are essential for enhancing future economic and environmental performance. Full article