Search Results (19,114)

Different topologies for individual stator teeth as modular electric motor components are investigated via several different metrics such as finite element analysis (FEA), winding methodologies, and thermal performance during electrical power loading. These are easily quantifiable metrics which allow for the direct comparison of the different topologies, particularly with respect to the concentrated windings of the copper wire around the stator teeth. The paper assesses temperature rise, heat dissipation, and the role of air gaps within the copper wire windings. The results show that the winding via robot resulted in 30 (±5)% lower temperature rises on average compared to commercial (hairpin) winding systems, due to more ordered winding which results in larger air gaps between the wires. The air gaps appear to play a critical role in the thermal performance of the stator windings. It is also shown that the different topologies affect thermal performance during electrical loading, suggesting that the different topologies could be useful in different applications. Full article

Thermal energy rejected through exhaust gases and cooling systems in marine propulsion units and conventional power plants represents a significant yet underutilized opportunity for improving energy efficiency and reducing carbon emissions. The Organic Rankine Cycle (ORC) has emerged as an effective technology for converting such waste heat into useful power using organic working fluids with favorable thermophysical properties. This study presents a comprehensive thermodynamic, economic, and exergo-economic evaluation of an ORC system incorporating single-stage and multistage turbine arrangements, using R245fa, R123, and R365mfc as working fluids. A validated cycle model is coupled with key economic indicators, including Net Present Value (NPV), Levelized Cost of Electricity (LCOE), and payback period, together with a simplified exergo-economic framework based on exergy destruction costs. The results demonstrate that implementing ORC-based waste heat recovery significantly enhances overall system performance by converting rejected thermal energy into electricity and improving thermal efficiency. Multistage turbine configurations further strengthen performance, increasing net power output and efficiency, with the multistage R245fa system generating more than 530,000 kWh annually. Economically, the single-stage R245fa configuration achieves the lowest LCOE (0.021 USD/kWh) and the shortest payback period, below eight years. Exergo-economic analysis shows that multistage turbines can reduce exergy destruction costs by more than 80%, with benefits becoming pronounced at heat source temperatures above 170 °C. Full article

To improve the fire safety performance of fire protection renovation projects for existing public buildings, this paper systematically sorts out and analyzes relevant research studies, accident reports, and fire protection renovation codes and guidelines. It constructs a fire performance evaluation system for such projects, including 4 first-level indicators—”Building Characteristics”, “Building Fire Protection and Rescue”, “Fire Facilities and Equipment”, and “Heating, Ventilation, Air Conditioning (HVAC) and Electrical Systems”—and 19 second-level indicators such as “Building Usage Function”. The subjective–objective combined weighting method of Analytic Hierarchy Process (AHP)-CRITIC is adopted to determine the weights of indicators at all levels. Four high-weight second-level indicators are selected as core remediation objects: average fire load density, floor layout, automatic fire alarm and linkage control system, and electrical systems. Meanwhile, the evaluation system is converted into a Bayesian Network model, with an empirical verification analysis carried out on a shopping mall in Chaoyang District, Beijing, as a case study. Results show that the approach of combining partial codes with the rectification of high-weight indicators can reduce the fire occurrence probability of the mall from 78%, before renovation, to 24%. Therefore, the constructed evaluation system and Bayesian Network model can realize the accurate quantification of fire risks, provide scientific and feasible technical schemes for the fire protection renovation of existing public buildings, and lay a foundation for enriching and improving fire protection assessment theories. Full article

Thermal control represents one of the most important parameters influencing the safety and reliability of lithium-ion batteries, especially at high rates required for modern electric vehicles. The present paper investigates the thermal and electrothermal performance of a lithium iron phosphate (LiFePO₄) battery pack using a combination of experimental, statistical, and numerical methods. The 8S5P module was assembled and examined under load tests of 200, 400, and 600 W with and without active air-based cooling. The findings indicate that cooling reduced cell surface temperature by up to 10 °C and extended discharge time by 7–16% under various load conditions, emphasizing the effect of thermal management on battery performance and safety. In order to more systematically investigate the impact of ambient temperature and load, a RSM study with a central composite design (CCD; 13 runs) was performed, resulting in two very significant quadratic models (R² > 0.98) for peak temperature and discharge duration prediction. The optimum conditions are estimated at a 200 W load and an ambient temperature of 20 °C. Based on experimentally determined parameters, a finite-element simulation model was established, and its predictions agreed well with the measured results, which verified the analysis. Integrating measurements, statistical modeling, and simulation provides a tri-phase methodology to date for determining and optimizing battery performance under the electrothermal dynamics of varied environments. Full article

Most electric vehicles use 400 V batteries, while some companies are moving to 800 V to reduce current in electric drives. More cars are expected to adopt 800 V at the DC terminals of the batteries, but 400 V will remain common for the duration of this transition, so future off-board chargers must support a wide voltage output range. Silicon carbide switches are used to keep the power–electronics interface compact and scalable. The AC/DC stage of a modular silicon carbide-based interface is designed using a T-type active front end and a dual active bridge. The T-type front end is optimized with a genetic algorithm. The resulting model is used to tune the inner current and outer voltage controllers. Bode analysis shows an inner current loop bandwidth of 4.25 kHz with a phase margin of ${53}^{\circ }$ and a gain margin of 30 dB. The outer voltage loop reaches 50 Hz with a phase margin of ${108}^{\circ }$ and a gain margin of 33 dB. The controller is implemented on a dSPACE MicroLabBox. Tests show peak efficiency of 98.5% in G2V mode and 98.3% V2G mode. THD stays under 5% above 4 kW and reaches 3% at peak power. Full article

In response to the urgent need for decarbonising the transport sector, this paper analyses the thermal performance of a battery electric minibus under cold ambient conditions. Thermal simulation models of the vehicle cabin and its electric heating circuits for both driver and passenger areas were developed using Modelica and validated with measurement data at −7 °C and 0 °C. The model showed good agreement with the measurements, with cabin temperature deviations within ±1.6 K and heating power deviations below 6%. Results show that the existing electric-only heating system is, in the automatic heating mode selected, insufficient to reach the target cabin temperature of 23 °C, as the optional fuel-powered heater was omitted to ensure fully zero-emission operation. To address this, an extended heating system with an additional heat exchanger was implemented in the simulation, which improved the overall cabin temperature level and also its spatial variation. However, it also increased the heating power demand by 43% at −7 °C (from 4.8 kW to 6.8 kW) and by 17% at 0 °C (from 4.8 kW to 5.6 kW). An additional heat loss analysis revealed that approx. 65–75% of all thermal losses occur through the window areas. Future improvements should therefore focus on optimising the heating strategy and enhancing cabin and heating system insulation to reduce energy demand while maintaining or even improving passenger comfort. Full article

Rising global electricity demand and the expansion of distribution networks require a critical assessment of component-level greenhouse gas contributions. Distribution transformers, although indispensable, have significant life-cycle carbon impacts due to the use of materials, manufacturing, and in-service losses. This study conducts a life-cycle assessment of a single-phase, 75 kVA oil-immersed distribution transformer manufactured in Newfoundland, one of the provinces with the cleanest, hydro-dominated grids in Canada, and evaluates it over a 40-year lifespan. Using a cradle-to-use boundary, the analysis quantifies embodied emissions from raw material extraction, manufacturing, and transportation, alongside operational emissions derived from empirically measured no-load and load losses. All the data are collected directly during the manufacturing process, ensuring high analytical fidelity. The energy efficiency of the transformer is analyzed in MATLAB version R2023b using measured no-load and load losses to generate efficiency, load characteristics under various operating conditions. Under varying load factor scenarios and based on Newfoundland’s 2025 grid intensity of 18 g CO₂e/kWh, the lifetime operational emissions are estimated to range from 0.19 t CO₂e under no-load operation to 4.4 t CO₂e under full-load conditions. A linear regression-based decarbonization model using Microsoft Excel projects grid intensity to reach net-zero around 2037, two years beyond the provincial target, indicating that post-2037 transformer losses will remain energetically relevant but carbon-neutral. Sensitivity analysis reveals that temporary overloading can substantially elevate lifetime emissions, emphasizing the value of smart-grid-enabled load management and optimal transformer sizing. Comparative assessment with fossil fuel-intensive provinces across Canada demonstrates the dominant influence of grid generation mix on life-cycle emissions. Additionally, refurbishment scenarios indicate up to 50% reduction in cradle-to-gate emissions through material reuse and oil reclamation. The findings establish a scalable framework for integrating grid decarbonization trajectories, life-cycle carbon modelling, and circular-economy strategies into sustainable distribution network planning and transformer asset management. Full article

With the large-scale integration of power electronic converters, non-linear loads, and renewable energy generation, voltage and current waveform distortion in modern power systems has become increasingly severe, making harmonic resonance amplification and non-stationary distortion more prominent. Accurate and robust harmonic-level prediction and detection have become essential foundations for power quality monitoring and operational protection. However, traditional harmonic analysis methods remain highly dependent on pre-designed time–frequency transformations and manual feature extraction. They are sensitive to noise interference and operational variations, often exhibiting performance degradation under complex operating conditions. To address these challenges, a Unified Physics-Transformer-based harmonic detection scheme is proposed to accurately forecast harmonic levels in offshore wind farms (OWFs). This framework utilizes real-world wind speed data from Bozcaada, Turkey, to drive a high-fidelity electromagnetic transient simulation, constructing a benchmark dataset without reliance on generative data expansion. The proposed model features a Feature Tokenizer to project continuous physical quantities (e.g., wind speed, active power) into high-dimensional latent spaces and employs a Multi-Head Self-Attention mechanism to explicitly capture the complex, non-linear couplings between meteorological inputs and electrical states. Crucially, a Multi-Task Learning (MTL) strategy is implemented to simultaneously regress the Total Harmonic Distortion (THD) and the characteristic 5th Harmonic (H5), effectively leveraging shared representations to improve generalization. Comparative experiments with Random Forest, LSTM, and GRU systematically evaluate the predictive performance using metrics such as root mean square error (RMSE) and mean absolute percentage error (MAPE). Results demonstrate that the Physics-Transformer significantly outperforms baseline methods in prediction accuracy, robustness to operational variations, and the ability to capture transient resonance events. This study provides a data-efficient, high-precision approach for harmonic forecasting, offering valuable insights for future renewable grid integration and stability analysis. Full article

Ge-doped CIGSe absorbers were fabricated using a two-step process of depositing sputtered stacked Ge-doped CIGSe precursors and selenization annealing. The effects of Ge doping on the crystallinity as well as defects of CIGSe absorbers and the performance of CIGSe buffer-free solar cells were investigated. The results show that Ge doping significantly promotes the grain growth of CIGSe absorbers. Due to Ge loss via volatilization during selenization annealing, Ge residue is undetectable in Ge-doped absorbers. Ge doping offers an effective approach to improve CIGSe crystallinity without introducing notable impurity phases or Ge-related defects. However, Ge doping also induces Se loss, and excessive Se vacancy defects adversely affect the performance of the absorber. In addition, Ge doping increases the contact potential difference at CIGSe grain boundaries and is beneficial for reducing carrier recombination at these sites. Analysis of recombination rates in Ge-doped CIGSe buffer-free solar cells reveals that the combined effects of enhanced crystallinity and optimized electrical properties at grain boundaries effectively suppress the recombination in the space charge region, at the interface, and in the quasi-neutral region, leading to improved device performance. Full article

The growing penetration of power electronics and nonlinear loads in industrial electrical networks has increased the dynamic complexity of these systems, exceeding the analysis capabilities of traditional approaches based on quasi-stationary models. In this context, this paper presents a methodology for the dynamic characterization of an industrial electrical network based on high-resolution synchrophasor measurements obtained using a microPMU. The proposed approach is based on the identification of a linear dynamic model in state space using subspace techniques based on real data recorded during a short-duration transient event. The results show that the identified model is capable of adequately capturing local underdamped dynamics and reproducing the temporal response observed in the measurements. This evidences the presence of dynamic modes associated with the interaction between the network and power electronics-based devices. Similarly, the stability analysis of the identified model demonstrates its consistency and robust gains in temporal variations within the analysis window. Overall, the results confirm that the combination of microPMU and data-based modeling techniques is an effective tool for improving dynamic observability and understanding the transient behavior of industrial power grids, complementing classical analysis and simulation methods. Full article

The expansion of renewables in modern power systems and the coordinated development of upstream and downstream industrial chains are promoting a shift on the utility side from traditional settlement by energy toward operation driven by data and models. Industrial electricity consumption data exhibit pronounced multi-scale temporal structures and sectoral heterogeneity, which makes unified long-term load and generation forecasting while maintaining accuracy, interpretability, and scalability a challenge. From a modern system identification perspective, this paper proposes a System Identification in Adaptive Bayesian Framework (SIABF) for medium- and long-term industrial load forecasting based on daily freeze electricity time series. By combining daily aggregation of high-frequency data, frequency domain analysis, sparse identification, and long-term extrapolation, we first construct daily freeze series from 15 min measurements, and then we apply discrete Fourier transforms and a spectral complexity index to extract dominant periodic components and build an interpretable sinusoidal basis library. A sparse regression formulation with ${\ell }_1$ regularization is employed to select a compact set of key basis functions, yielding concise representations of sector and enterprise load profiles and naturally supporting multivariate and joint multi-sector modeling. Building on this structure, we implement a state-space-implicit physics-informed Bayesian forecasting model and evaluate it on real data from three representative sectors, namely, steel, photovoltaics, and chemical, using one year of 15 min measurements. Under a one-month-ahead evaluation using one year of 15 min measurements, the proposed framework attains a Mean Absolute Percentage Error (MAPE) of 4.5% for a representative PV-related customer case and achieves low single-digit MAPE for high-inertia sectors, often outperforming classical statistical models, sparse learning baselines, and deep learning architectures. These results should be interpreted as indicative given the limited time span and sample size, and broader multi-year, population-level validation is warranted. Full article

Research on road transport infrastructures is rapidly evolving as electrification, automation, and digital connectivity reshape how systems are designed, operated, and managed. This study presents a combined bibliometric, scientometric, and network analysis of 2755 publications published between 2021 and 2025 to map the intellectual structure, main contributors, and dominant technological themes shaping contemporary road transport research. Using data from the Web of Science Core Collection, co-occurrence mapping, thematic analysis, and collaboration networks were generated using Bibliometrix and VOSviewer. The results reveal strong growth in research output, with China, the United States, and Europe forming the core of high-impact publication and collaboration networks. Six bibliometric clusters were identified and consolidated into three overarching domains: road transport systems, emphasizing vehicle dynamics, control, and real-time computational frameworks; energy and efficiency-oriented mobility research, focusing on electrification, optimization, and infrastructure integration; and emerging digital technologies, including IoT, AI, and autonomous vehicles. The analysis highlights persistent research gaps related to interoperability, cybersecurity, large-scale deployment, and governance of intelligent transport infrastructures. Overall, the findings provide a data-driven overview of current research priorities and structural patterns shaping next-generation road transport systems. Full article

Under the “dual carbon” strategy and green energy transition, traditional static accounting models have coarse temporal granularity. These models cannot meet the needs of fine-grained management and dynamic control for industrial parks. Therefore, it is urgent to develop high-resolution dynamic accounting systems and analyze model uncertainty. This study first defines the carbon source structure and establishes the accounting boundary for industrial parks. Second, it proposes dynamic accounting methods for both direct and indirect carbon emissions. Third, the study develops an uncertainty analysis model that considers parameter variability and error propagation. Finally, the feasibility and effectiveness of the proposed method are validated through a case study of a typical industrial park in Sichuan Province, China. The results indicate that the overall uncertainty of carbon emissions in the park is 28.9%, with electricity consumption identified as the primary driver of uncertainty (Spearman correlation coefficient of 0.986). The proposed framework effectively captures real-time emission fluctuations, providing a scientific basis for fine-grained carbon management. Full article

Background/Objectives: The cerebellum plays a central role in sensorimotor integration and temporal processing, yet its direct electrophysiological investigation in humans remains challenging, and cerebellar contributions to somatosensory responses remain poorly defined. This study aimed to determine whether cerebellar responses to peripheral nerve stimulation can be detected using scalp EEG and whether time–frequency analysis provides advantages over time-domain approaches. Methods: Scalp EEG was recorded during electrical stimulation of the median nerve and tibial nerve in 16 healthy participants. Electrode montages included posterior fossa placements targeting cerebellar activity, together with standard cortical and subcortical derivations. Data were analyzed in the time domain using evoked potentials and channel comparisons, including bipolar cerebellar derivations, and in the time–frequency domain using spectral power analysis. Results: Time-domain analyses revealed early and intermediate latency components following both upper- and lower-limb stimulation; however, these responses showed limited spatial specificity and were strongly influenced by reference effects and subcortical contamination. In contrast, time–frequency analysis consistently revealed sustained increases in oscillatory power in cerebellar channels. Power increases emerged approximately 50 ms after stimulation and persisted beyond 300 ms, peaking around ~20 Hz for upper-limb stimulation and ~10 Hz for lower-limb stimulation, with evidence of side specificity. Conclusions: Non-invasive EEG can detect cerebellar responses to peripheral nerve stimulation, particularly in the time–frequency domain. Oscillatory dynamics provide a more robust marker of cerebellar involvement than time-locked responses and may complement conventional somatosensory evoked potentials in studies of cerebellar physiology and spinocerebellar pathway integrity. Full article

This article examines public preferences for a proposed West Coast submarine high-voltage direct current (HVDC) transmission network in South Korea, installed by trenching and burying the cables in the seabed, which is essential for facilitating renewable energy integration and ensuring a stable electricity supply to the Seoul Metropolitan Area. The purpose of this study is to estimate South Korean households’ willingness to pay (WTP) for the proposed West Coast submarine HVDC network using contingent valuation (CV), thereby assessing its social acceptability amid renewable energy integration challenges. Employing a CV survey with a nationally representative sample of 1000 households conducted from late May to late June 2025, the research applies the one-and-one-half-bound spike model to address zero WTP responses and incorporates socio-demographic covariates to account for preference heterogeneity. The analysis estimates an average monthly WTP of KRW 1832 (USD 1.33) per household for the HVDC infrastructure. Results demonstrate statistically significant public support for the submarine HVDC project despite its high capital investment and potential electricity rate increases. These findings underscore notable consumer acceptance and provide valuable welfare insights for policymakers, reinforcing the prioritization of this project within South Korea’s energy transition framework. This paper contributes to the field of energy infrastructure valuation by advancing methodological approaches and offering policy-relevant recommendations for sustainable grid expansion. Full article