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The electronic and electrical architecture of vehicles has rapidly evolved to centralized. At present, there is no unified consensus on the R&D strategy of the platform in the industry, and there is also a lack of a quantitative decision-making framework that can be implemented. This study takes the centralized architecture platform as the research object, constructs a two-stage analysis framework of “advanced constraint screening-cost quantitative evaluation”, uses a fuzzy-set qualitative comparative analysis method to screen feasible R&D strategy combinations that meet the requirements of the architectural advancement, builds a total cost of ownership evaluation system around the software and hardware elements related to the architecture platform, and systematically analyzes the optimal cost R&D strategy combinations of car enterprises with different mass production scales under the two scenarios of Multi-Box and One-Board. The research results show that adaptive platform middleware and framework middleware are the core necessary elements to realize the advanced architecture; the amortization cost of architecture is negatively correlated with the scale of mass production, and the cost of in-house R&D is highly dependent on large-scale amortization; and there are differentiated optimal solutions in the framework selection and R&D strategy combination of automakers with different mass production scales. This study can provide quantitative reference and practical guidance for R&D decision making of centralized architecture platform for automakers. Full article

Short-term load forecasting is essential for the reliable, secure, efficient, and economic operation of modern power systems and electricity markets. Among many forecasting strategies, the similar day (SD) approach for short-term load forecasting was among the earliest used to assess power demand and remains one of the most intuitive and widely adopted techniques worldwide. However, over time, increasing system complexity, richer datasets, and advances in computational intelligence have led to the evolution of SD methodologies beyond heuristic-based rule formulations. This work presents a study of the relevant literature on short-term load forecasting using SD methods reported between 2000 and 2025. This study analyzes how similarity is defined, how forecasts are generated, and how both stages interact within the complete forecasting process in the reviewed literature. Based on these criteria, a unified taxonomy is proposed to classify SD methods into conventional, intelligent, and hybrid formulations. This study provides insight into the methodologies, their performance, and the systems in which they have been tested. The results show that SD-based approaches remain competitive for short-term forecasting and that incorporating artificial intelligence techniques can further enhance their accuracy. Full article

Nitrogen (N) and phosphorus (P) are essential nutrients that play critical roles in plant physiological processes and the accumulation of N and P in broccoli head was significantly correlated with yield. Therefore, there is a need for a rapid, non-destructive diagnosis of crop status by detecting deficiencies in essential nutrients. This study evaluated the effects of N and P deficiency on field grown broccoli (Brassica oleracea L. var. italica Plenk) using a plant-induced electrical signal (PIES) sensor, in which needle electrodes are inserted into the stem to measure electrical conductivity reflecting plant water and ion status. Four treatments were established, including the control (N100P100) with sufficient N and P supply, N deficiency (N0P100), P deficiency (N100P0), and combined N–P deficiency (N0P0). For sufficient supply, urea and fused phosphate (FP) were applied at rates of 122 kg N ha⁻¹ and 71 kg P ha⁻¹, respectively. Soil, stem, and leaf nutrient contents, growth parameters, and stress related indicators were analyzed and their relationship with PIES values were evaluated. PIES was highest in control (N100P100) and lowest under N–P deficiency (N0P0). Higher PIES values were observed during the vegetative stage, whereas values declined during the reproductive stage, reflecting changes in physiological activity. Growth parameters such as shoot and root weight and stem diameter were generally superior in the control (N100P100) treatment, while leaf calcium (Ca), magnesium (Mg), and potassium (K) concentrations showed no significant differences among treatments. Total N content in leaves was higher in N fertilized treatments (control and P deficiency). Photosynthesis-related parameters, including soil plant analysis development (SPAD), Fv/Fm, and chlorophyll content, were lowest under N–P deficiency, which was reflected in the PIES. Principal component analysis (PCA) showed that the PIES was closely associated with growth and photosynthetic parameters and clearly distinguished N sufficient treatments (control and P deficiency) from N deficient treatments (N0P100, N0P0). Overall, these findings suggest that PIES monitoring can serve as a sensitive physiological indicator of nutrient stress and may be applied as an early diagnostic tool before visible growth inhibition occurs in broccoli cultivation. Full article

In the aviation sector, there is a growing demand for high-specific-power electrical machines to realize More Electric Aircraft (MEA). The goals for these machines were set by the National Aeronautics and Space Administration (NASA) as 1 $\mathrm{M}$$\mathrm{W}$ power, >13 kW kg⁻¹ of power density, and efficiency >96%. To address these requirements, this paper proposes an electromagnetic design of a high-speed, power-dense, 1 $\mathrm{M}$$\mathrm{W}$ radial-flux Permanent Magnet Synchronous Machine (PMSM) for aerospace propulsion applications that achieves NASA targets. Achieving high-specific-power objectives necessitates geometry optimization that simultaneously minimizes motor mass while maximizing output power. This paper presents a faster optimization algorithm that hybridizes Genetic Algorithm and Artificial Neural Network (ANN)-based surrogate modeling to optimize the motor for multi-objective goals. The proposed framework employs a multi-objective approach targeting maximum torque output and efficiency within a minimum motor mass. This approach, using an ANN-based surrogate, significantly reduces optimization time by saving 95% of the time compared to FEM simulations. The optimized 1 $\mathrm{M}$$\mathrm{W}$ motor attains 98% efficiency and an active power density of 24.87 kW kg⁻¹. The various stages of the optimization are presented in detail and a comparison of the time saving using the proposed algorithm is outlined. To demonstrate the feasibility of design, a detailed electromagnetic analysis, stator thermal analysis with a jet impingement design, and magnet demagnetization risk analysis were also presented. Full article

Long-term energy sustainability in the built environment depends not only on deploying renewables but also on maintaining high energy efficiency that consistently lowers demand and enables more effective use of low-carbon electricity over time. This paper presents an illustrative case study that demonstrates a low-data, EPC/audit-based screening workflow for assessing operational energy, carbon, and water-related indicators in a non-residential building. An explanatory case study is conducted for a mixed-use logistics facility in Poland (≈610 m²), combining approaches to useful/final/primary energy indicators with operational carbon and water footprints. The operational water footprint is evaluated as a screening metric (L/kWh) applied to the annual electricity balance and tested across PV self-consumption levels (25/50/75%) to reflect the role of energy management and flexibility. The results indicate that an efficiency-oriented modernization pathway supported by PV integration (≈64 kWp; ~57,350 kWh/yr) reduces the primary energy performance indicator EP from 154 to 62.5 kWh/m²·yr, corresponding to a 59% reduction in annual primary energy demand. The operational water footprint indicator decreases nearly linearly with increasing PV self-consumption, demonstrating that long-term benefits depend on sustained efficiency and on maximizing on-site renewable utilization through controls, demand shifting, and/or storage. Overall, the framework supports transparent benchmarking and the development of staged pathways for integrating renewable and low-carbon energy systems into logistics-building portfolios, while maintaining an analytical focus on operational energy, carbon, and water performances. Full article

This article examines the impact of intermodal wagon technical specifications and railway infrastructure parameters on electricity consumption in rail freight transport. For this purpose, a three-stage analytical model was developed. The first stage defines the core assumptions, including train length, rolling stock types, container configurations, infrastructure constraints, and the characteristics of the energy consumption model. The second stage identifies the technical constraints of specific wagons, determines representative train compositions, and performs loading simulations. The third stage evaluates energy efficiency across different loading scenarios. The case study shows that specific energy consumption varies significantly with wagon type, train mass, and route characteristics. This findings challenge the use of static energy consumption values commonly applied in the literature. The results indicate that 40-foot wagons incur high energy penalties due to their tare weight and axle count, despite offering high loading capacity. While 60-foot wagons consume less energy, they lead to a high share of empty slots under a 20 t/axle limit. In contrast, 80-foot wagons are the most energy-efficient, particularly at a 22.5 t/axle limit. Mixed consists provide a balance between operational flexibility and competitive performance. Extending train length from 600 m to 730 m increases volume but does not automatically reduce unit energy consumption. These findings highlight the need to align wagon fleet selection with infrastructure capabilities and cargo characteristics. This study therefore provides practical recommendations for planning energy-efficient intermodal operations. Full article

Residential air conditioning systems are a major contributor to household electricity consumption in tropical regions, where environmental factors such as climate variability and particulate pollution (PM10) can further increase cooling demand and accelerate equipment degradation. This study proposes an Enhanced Deep Q-Network (Enhanced DQN) for energy-efficient and maintenance-aware control of residential split-type air conditioners under dynamic environmental conditions. The proposed method integrates several stability-oriented reinforcement learning mechanisms, including Double Q-learning, a dueling architecture, prioritized experience replay, multi-step returns, Bayesian-style regularization via Monte Carlo dropout, and entropy-aware exploration. The framework is evaluated through a two-stage process consisting of a diagnostic benchmark on LunarLander-v3 to assess learning stability, followed by a realistic 365-day simulation driven by Thai weather and PM10 data. Compared with a fixed 25 °C baseline, the proposed controller reduced annual electricity consumption from 5116.22 kWh to as low as 4440.03 kWh, corresponding to a saving of 13.22%. The learned policy also exhibited environmentally adaptive behavior under high PM10 conditions, indicating maintenance-aware characteristics. These findings demonstrate that reinforcement learning can provide robust, adaptive, and sustainable control strategies for residential cooling systems in tropical environments. Full article

The gimbal servo system in a control moment gyroscope (CMG) is critical for high-precision spacecraft attitude control, where comprehensive performance testing and evaluation are essential for ensuring spacecraft reliability and service life. Traditional motor testbenches exhibit limitations, whereas the electric motor emulator (EME) based on power electronic converters is a promising alternative for testing extreme operating conditions, such as ultra-low speed operation and fault scenarios. However, existing EME control methods suffer from limited system bandwidth and insufficient emulation accuracy, which limits their applicability. To address these issues, this paper proposes an improved current control strategy for the ultra-low speed permanent magnet synchronous motor (PMSM) emulator. First, a mathematical model of the EME based on the topology of the voltage source converter is established. Then, based on the deadbeat control concept, a deadbeat predictive current control (DPCC) strategy is developed to enhance the dynamic performance. Furthermore, to suppress the parameter mismatch disturbance, an optimization scheme based on a cascaded extended state observer (CESO) is introduced. The first-stage ESO is applied to estimate and compensate for total disturbances, while the second-stage ESO is a supplement to suppress the remaining disturbances in the EME system, which improves the robustness of the DPCC controller. Finally, the effectiveness of the improved emulation accuracy of the proposed method is verified through experiments. Full article

The large-scale integration of renewable energy necessitates enhanced flexibility in power grids. As aggregators, electric vehicle charging stations (EVCSs) can provide potential grid services via vehicle-to-grid (V2G) technology. Against the challenge from the intertwined uncertainties of transmission system operation and renewable energy output limit, the private ownership of EVCSs limit their practical implementation. To exploit the flexibility of EVCSs to cope with the system operational uncertainties, this paper proposes a novel multi-stage coordination framework for EVCS flexibility utilization, based on a demand response incentive mechanism. The framework explicitly incorporates the operational constraints and charging/discharging strategies of EVCSs into the demand response clearing and dispatch mechanism. Specifically, adaptive robust optimization (ARO) and distributionally robust optimization (DRO) are employed to model the heterogeneous uncertainties of transmission operational requirements and renewable energy output, respectively. The model is solved in a distributed manner using the alternating direction method of multipliers (ADMM), with a tailored column-and-constraint generation (C&CG) algorithm developed to solve the regional problems. Simulation results confirm that the proposed method improves both economic efficiency and renewable energy accommodation. Full article

This study investigated the feasibility of a wearable wind energy-harvesting system integrated into a motorcycle helmet that converts riding-induced airflow into storable electrical energy. A compact horizontal-axis turbine-based system was designed and optimized through staged experiments focusing on generator selection, housing geometry, rotor configuration, and circuit-connected performance. A medium-scale generator, diffuser-type housing (Hd), and eight-blade pinwheel rotor (Rb) were identified as the most suitable combination for helmet-scale integration. The final prototype incorporated two side-mounted turbine modules, a crown-mounted harvesting–boost circuit, and a detachable rechargeable battery pack within a full-face helmet platform. In a field-based riding experiment, the prototype produced mean outputs of 3.99 V, 39.51 mA, and 157.64 mW at 30 km/h; 4.43 V, 43.48 mA, and 192.61 mW at 40 km/h; and 5.45 V, 53.53 mA, and 291.73 mW at 50 km/h. A static wearability evaluation with six participants indicated no obvious discomfort under a quasi-riding posture. These findings support the practical feasibility of helmet-integrated wind energy harvesting as an auxiliary power source for low-power wearable electronics, while highlighting the need for future studies on aerodynamic validation, dynamic wearability, acoustic burden, and safety-oriented structural refinement. Full article

As the global imperative for climate neutrality intensifies, hydrogen (H₂) from fossil fuels remains central to decarbonizing hard-to-abate sectors. Conventional production via steam methane reforming (SMR), however, is carbon-intensive and, even with carbon capture and storage (CCS), incurs energy penalties and long-term storage constraints. This review develops a harmonized well-to-gate, market-oriented framework to evaluate methane pyrolysis (MP) relative to SMR and autothermal reforming (ATR), with or without CCS, moving beyond reactor-focused assessments toward system-level commercialization analysis. MP decomposes methane into hydrogen and solid carbon, avoiding direct CO₂ formation and the need for CCS infrastructure. Integrating with the reverse water–gas shift (RWGS) reaction enables flexible syngas production with adjustable H₂:CO ratios for methanol and chemical synthesis. A central finding is the dominant role of the “carbon lever”: MP generates approximately 3 kg of solid carbon per kg of H₂, making the carbon market’s absorptive capacity the primary scalability constraint. While carbon monetization can reduce levelized hydrogen costs, large-scale deployment would rapidly saturate existing carbon black and specialty carbon markets. Techno-economic evidence indicates that carbon prices above $500/ton are required to achieve parity with gray hydrogen, whereas $150–200/ton enables competitiveness with blue hydrogen. Lifecycle assessments further show that climate superiority over SMR or ATR with CCS requires upstream methane leakage below 0.5% and very low-carbon electricity. Commercial readiness varies, with plasma MP at TRL 8–9 and thermal, catalytic, and molten-media pathways remaining at the pilot or demonstration stage. Parametric decision-space analysis under harmonized boundary assumptions shows that MP is not a universal substitute for reforming but a conditional pathway competitive only under aligned conditions of low-leakage gas supply, low-carbon electricity, credible carbon monetization, and supportive policy incentives. The review concludes with a roadmap that highlights standardized carbon certification, end-of-life accounting, and long-duration operational data as priorities for commercialization. Full article

With high wind power penetration, power system operations face significant uncertainty, rendering traditional pricing mechanisms inadequate for stochastic dispatch environments and hindering the sustainable development of power systems with high renewable energy integration. This paper systematically compares three electricity pricing schemes—system marginal pricing, conservative pricing, and the proposed average pricing—within a two-stage stochastic unit commitment framework. It is found that system marginal pricing behaves as an ex post pricing method dependent on scenario realizations and lacks stability, whereas conservative pricing degenerates into a scheme based on the minimum wind output scenario, leading to higher and more volatile prices. To address these issues, this paper proposes a novel “Average Pricing” method, in which the day-ahead price is defined as the expected value of marginal prices across all wind power scenarios. Theoretical analysis and numerical simulations on the IEEE 39-bus system demonstrate that the proposed method offers both economic interpretability and numerical stability, with mean prices ranging from 14.0739 to 15.9825 and standard deviations ranging from 16.6323 to 19.9471 across four seasonal cases. Compared with conservative pricing, it achieves lower mean prices in three seasons and lower price volatility in three seasons while maintaining a unique day-ahead price and providing a novel and sustainable pathway for pricing design in power systems with high renewable energy integration. Full article

Achieving sustainability requires that energy transition generates measurable environmental benefits beyond the power sector, yet it remains unclear whether renewable electricity expansion is associated with lower waste sector methane intensity, a major source of short-lived climate forcing. Using a global country–year panel and two-way fixed effects, we examine whether this relationship, and its sustainability implications, varies with development stage, institutional quality, and waste system characteristics. We find no robust inverted-U Environmental Kuznets Curve once country and year fixed effects are included. Instead, higher renewable electricity shares are consistently associated with lower waste sector methane intensity, and this association strengthens with income. A 10-percentage-point increase in renewable share corresponds to about 2.7%, 4.2%, and 6.0% lower intensity at the 25th, 50th, and 75th income percentiles. The negative association is stronger in countries with higher governance quality, while waste management capacity and organic waste composition reveal additional heterogeneity in the observed association. Overall, electricity decarbonization alone is not a uniform instrument for reducing diffuse biological emissions; sustainable methane mitigation likely requires coordinated governance linking renewable transition with waste system modernization. Full article

The design of millimeter-scale micro-turbine blades is challenging due to conflicting requirements: achieving aerodynamic performance while remaining compatible with microfabrication, and exploring high-dimensional morphological design spaces without prohibitive computational cost. To address these challenges, this study proposes an intelligent framework for the design and optimization of the three-dimensional blade profile of a 3 mm diameter micro-turbine. The blade morphology is parameterized using 22 variables, ensuring geometric feasibility for micro-EDM (Electrical Discharge Machining) fabrication. A physics-informed neural network (PINN) surrogate model, efficiently trained through a two-stage active learning strategy combining KD-tree exploration and residual-based sampling, provides accurate predictions of flow fields. Multi-objective optimization using Non-dominated Sorting Genetic Algorithm II (NSGA-II) is then performed to maximize torque and thrust. Experimental results show that the optimized blade achieves a 38.6% increase in rotational speed while retaining 75.1% of thrust at 0.2 MPa inlet pressure, validating the framework’s effectiveness. This methodology offers a systematic solution for designing microfluidic devices characterized by high-dimensional parameters and high-fidelity simulation requirements. Full article

This study presents a stator-focused electromagnetic optimization of a 350 W, 27-slot, 30-pole outer-rotor brushless direct current (BLDC) motor developed for electric scooter applications. Unlike conventional redesign approaches that modify rotor topology or overall motor dimensions, the proposed methodology preserves the rotor structure and external geometry of a commercially validated reference motor and improves performance primarily through targeted stator geometric refinement, with minor adjustments in the winding configuration. A two-stage optimization strategy combining parametric analysis and genetic algorithm (GA)-based multi-objective optimization is implemented to minimize cogging torque and torque ripple while maximizing efficiency. Finite element analyses (FEA) were conducted to evaluate back electromotive force (back-EMF) characteristics, magnetic flux density distribution, torque behavior, and current density. Experimental validation confirms a 54.86% reduction in cogging torque (from 257 mNm to 116 mNm), a 19.6% decrease in torque ripple, a 6.17% reduction in maximum current density, and a 2–3% improvement in efficiency within the nominal load range (5.2–6.45 Nm), reaching 85.69% efficiency at 350 W output power. The results demonstrate that systematic stator geometry optimization, supported by minor winding modifications, can significantly enhance efficiency, torque smoothness, and thermal margin without increasing motor size, rated power, or manufacturing complexity. This work provides a practical and manufacturable design pathway for high-performance outer rotor BLDC motors in light electric vehicle (LEV) propulsion systems. Full article