Search Results (291)

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

Dark asphalt surfaces, absorbing about 95% of solar radiation and warming to 60–70 °C during summer, intensify urban heat while providing substantial prospects for energy extraction. This review evaluates four primary technologies—asphalt solar collectors (ASCs, including phase change material (PCM) integration), photovoltaic (PV) systems, vibration-based harvesting, thermoelectric generators (TEGs)—focusing on their principles, efficiencies, and urban applications. ASCs achieve up to 30% efficiency with a 150–300 W/m² output, reducing pavement temperatures by 0.5–3.2 °C, while PV pavements yield 42–49% efficiency, generating 245 kWh/m² and lowering temperatures by an average of 6.4 °C. Piezoelectric transducers produce 50.41 mW under traffic loads, and TEGs deliver 0.3–5.0 W with a 23 °C gradient. Applications include powering sensors, streetlights, and de-icing systems, with ASCs extending pavement life by 3 years. Hybrid systems, like PV/T, achieve 37.31% efficiency, enhancing UHI mitigation and emissions reduction. Economically, ASCs offer a 5-year payback period with a USD 3000 net present value, though PV and piezoelectric systems face cost and durability challenges. Environmental benefits include 30–40% heat retention for winter use and 17% increased PV self-use with EV integration. Despite significant potential, high costs and scalability issues hinder adoption. Future research should optimize designs, develop adaptive materials, and validate systems under real-world conditions to advance sustainable urban infrastructure. Full article

This study investigates a lithium-ion battery failure in heating insoles that ignited during normal walking while powered off. Through comprehensive material characterization, electrical testing, thermal analysis, and mechanical gait simulation, we systematically excluded electrical or thermal abuse as failure causes. X-ray/CT imaging localized the ignition source to the lateral heel edge of the pouch cell, correlating precisely with peak mechanical stress identified through gait analysis. Remarkably, the cyclic load was less than 10% of the single crush load threshold specified in safety standards. Key findings reveal multiple contributing factors as follows: the uncoated polyethylene separator’s inability to prevent stress-induced internal short circuits, the circuit design’s lack of battery health monitoring functionality that permitted undetected degradation, and the hazardous placement inside clothing that exacerbated burn injuries. These findings necessitate a multi-level safety framework for lithium-ion battery products, encompassing enhanced cell design to prevent internal short circuit, improved circuit protection with health monitoring capabilities, optimized product integration to mitigate mechanical and environmental impact, and effective post-failure containment measures. This case study exposes a critical need for product-specific safety standards that address the unique demands of wearable lithium-ion batteries, where existing certification requirements fail to prevent real-use failure scenarios. Full article

The construction sector presently consumes about 40% of global energy and generates 36% of CO₂ emissions, making facade retrofits a priority for decarbonising buildings. This review clarifies how ventilated facades (VFs), wall assemblies that interpose a ventilated air cavity between outer cladding and the insulated structure, address that challenge. First, the paper categorises VFs by structural configuration, ventilation strategy and functional control into four principal families: double-skin, rainscreen, hybrid/adaptive and active–passive systems, with further extensions such as BIPV, PCM and green-wall integrations that couple energy generation or storage with envelope performance. Heat-transfer analysis shows that the cavity interrupts conductive paths, promotes buoyancy- or wind-driven convection, and curtails radiative exchange. Key design parameters, including cavity depth, vent-area ratio, airflow velocity and surface emissivity, govern this balance, while hybrid ventilation offers the most excellent peak-load mitigation with modest energy input. A synthesis of simulation and field studies indicates that properly detailed VFs reduce envelope cooling loads by 20–55% across diverse climates and cut winter heating demand by 10–20% when vents are seasonally managed or coupled with heat-recovery devices. These thermal benefits translate into steadier interior surface temperatures, lower radiant asymmetry and fewer drafts, thereby expanding the hours occupants remain within comfort bands without mechanical conditioning. Climate-responsive guidance emerges in tropical and arid regions, favouring highly ventilated, low-absorptance cladding; temperate and continental zones gain from adaptive vents, movable insulation or PCM layers; multi-skin adaptive facades promise balanced year-round savings by re-configuring in real time. Overall, the review demonstrates that VFs constitute a versatile, passive-plus platform for low-carbon buildings, simultaneously enhancing energy efficiency, durability and indoor comfort. Future advances in smart controls, bio-based materials and integrated energy-recovery systems are poised to unlock further performance gains and accelerate the sector’s transition to net-zero. Emerging multifunctional materials such as phase-change composites, nanostructured coatings, and perovskite-integrated systems also show promise in enhancing facade adaptability and energy responsiveness. Full article

Urban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas, and hydrogen. This paper presents an exergy-enhanced stochastic optimization framework for the optimal scheduling of electricity–hydrogen urban integrated energy systems (EHUIESs) under multiple uncertainties. By incorporating exergy efficiency evaluation into a Stochastic Optimization–Improved Information Gap Decision Theory (SOI-IGDT) framework, the model dynamically balances economic cost with thermodynamic performance. A penalty-based iterative mechanism is introduced to track exergy deviations and guide the system toward higher energy quality. The proposed approach accounts for uncertainties in renewable output, load variation, and Hydrogen-enriched compressed natural gas (HCNG) combustion. Case studies based on a 186-bus UIES coupled with a 20-node HCNG network show that the method improves exergy efficiency by up to 2.18% while maintaining cost robustness across varying confidence levels. These results underscore the significance of integrating exergy into real-time robust optimization for resilient and high-quality energy scheduling. Full article

The microbiological safety of milk can be ensured through heat processing; however, this method has a negative effect on the sensory profile of this food product. Emerging technologies could be used as an alternative process for guaranteeing innocuity and maintaining sensory changes. An alternative is to evaluate pulsed electric field (PEF) electroporation, which is a method of processing cells using short pulses of a strong electric field. PEF has the potential to be a type of alternative low-temperature pasteurization process that consists of high-frequency voltage pulsations. Specifically, the presented work is a proof of concept for the design of a converter capable of generating a PEF to feed a load that meets the impedance characteristics of milk. The proposed converter is simulated using PLECS software (4.9.6 version) under impedance change scenarios that emulate variations in milk throughout the entire process. This research proposes the modification of a classic Vienna rectifier (adding an MBC—Multilevel Boost Converter structure) to supply a pulsating signal that could be used for low-temperature processes of milk to guarantee proper pasteurization. The characteristics of the generated high-voltage pulse make it feasible to quickly process the real sample. The control law design considers a regulation loop to achieve a voltage in the range of kV and a switching-type control law that activates switches in MMC arrays. These switches are activated randomly to avoid transients that cause significant stress on them. Full article

With the growing demand for global energy transition, integrated energy systems (IESs) have emerged as a key pathway for sustainable development due to their deep coupling of multi-energy flows. Accurate load forecasting is crucial for IES optimization and scheduling, yet conventional methods struggle with complex spatio-temporal correlations and long-term dependencies. This study proposes ST-ScaleFusion, a multi-scale time–frequency complementary hybrid model to enhance comprehensive energy load forecasting accuracy. The model features three core modules: a multi-scale decomposition hybrid module for fine-grained extraction of multi-time-scale features via hierarchical down-sampling and seasonal-trend decoupling; a frequency domain interpolation forecasting (FI) module using complex linear projection for amplitude-phase joint modeling to capture long-term patterns and suppress noise; and an FI sub-module extending series length via frequency domain interpolation to adapt to non-stationary loads. Experiments on 2021–2023 multi-energy load and meteorological data from the Arizona State University Tempe campus show that ST-ScaleFusion achieves 24 h forecasting MAE values of 667.67 kW for electric load, 1073.93 kW/h for cooling load, and 85.73 kW for heating load, outperforming models like TimesNet and TSMixer. Robust in long-step (96 h) forecasting, it reduces MAE by 30% compared to conventional methods, offering an efficient tool for real-time IES scheduling and risk decision-making. Full article

Heating, ventilation, and air conditioning (HVAC) systems and microgrids have garnered significant attention in recent research, with temperature control and renewable energy integration emerging as key focus areas in urban distribution power systems. This paper proposes a robust predictive temperature control (RPTC) method and a microgrid control strategy incorporating asymmetrical challenges, including uneven power load distribution and uncertainties in renewable outputs. The proposed method leverages a thermodynamics-based R-C model to achieve precise indoor temperature regulation under external disturbances, while a multisource disturbance compensation mechanism enhances system robustness. Additionally, an HVAC load control model is developed to enable real-time dynamic regulation of airflow, facilitating second-level load response and improved renewable energy accommodation. A symmetrical power tracking and voltage support secondary controller is also designed to accurately capture and manage the fluctuating power demands of HVAC systems for supporting operations of distribution power systems. The effectiveness of the proposed method is validated through power electronics simulations in the Matlab/Simulink/SimPowerSystems environment, demonstrating its practical applicability and superior performance. Full article

In this study, the cooling performance of fuel cell electric vehicles (FCEVs) with regard to thermal derating is investigated. Particularly in hot climate conditions, low operating temperature of the fuel cell stack and hence low temperature difference to the environment can result in thermal derating of the fuel cell stack. Experimental investigations on a production vehicle with a fuel cell drive (Hyundai Nexo) are conducted to analyze the influence of climatic boundary conditions and a dynamic driving scenario on the thermal management system of the vehicle. Therefore, a new method based on energy balances is introduced to indirectly measure the average cooling air velocity at the cooling module. The results indicate that the two high-power radiator fans effectively maintain a high cooling airflow between a vehicle speed of approximately 30 and 100 $\mathrm{k}\mathrm{m}/\mathrm{h}$, leading to efficient heat rejection at the cooling module largely independent of vehicle speed. Furthermore, this study reveals that the efficiency of the fuel cell system is notably affected by ambient air temperature, attributed to the load on the electric air compressor (EAC) as well as on cooling system components like cooling pump and radiator fans. However, at the stack level, balance of plant (BoP) components demonstrate the ability to ensure ambient temperature-independent performance, likely due to reliable humidification control up to 45 °C. Additionally, a new method for determining thermal derating of FCEVs on roller dynamometer tests is presented. A real-world uphill drive under ambient temperatures exceeding 40 °C demonstrates derating occurring in 6.3% of the time, although a worst case with an aged stack and high payload is not investigated in this study. Finally, a time constant of 50 $\mathrm{s}$ is found to be suitable to correlate the average fuel cell stack power with a coolant temperature at the stack inlet, which gives information on the thermal inertia of the system observed and can be used for future simulation studies. Full article

This paper aims to enhance the success of the Wild Horse Optimization (WHO) algorithm in optimization processes by developing strategies to overcome the issues of stuckness and early convergence in local spaces. The performance change is observed through a Multi-Layer Perceptron (MLP) sample. In this context, an advanced Wild Horse Optimization (IWHO) algorithm with a random walking strategy was developed to provide solution diversity in local spaces using a random walking strategy. Two challenging test sets, CEC 2019, were selected for the performance measurement of IWHO. Its competitiveness with alternative algorithms was measured, showing that its performance was superior. This superiority is visually represented with convergence curves and box plots. The Wilcoxon signed-rank test was used to evaluate IWHO as a distinct and powerful algorithm. The IWHO algorithm was applied to MLP training, addressing a real-world problem. Both WHO and IWHO algorithms were tested using MSE results and ROC curves. The Energy Efficiency Problem dataset from UCI was used for MLP training. This dataset evaluates the heating load (HL) or cooling load (CL) factors by considering the input characteristics of smart buildings. The goal is to ensure that HL and CL factors are evaluated most efficiently through the use of HVAC technology in smart buildings. WHO and IWHO were selected to train the MLP architecture, and it was observed that the proposed IWHO algorithm produced better results. Full article

In recent years, new fire loads dominated by power banks have caused multiple fire incidents in transportation hubs, posing severe challenges to fire safety. This study uses experimental testing and machine learning regression analysis to explore the heat release rate (HRR) characteristics and influencing factors of box-type power banks under fire conditions. A 1 MW calorimeter was used to conduct four sets of experiments involving a total of 15 box-type power banks, measuring the HRR and analyzing its correlation with oxygen consumption, carbon dioxide generation, smoke temperature, and the fire growth rate. Based on the experimental data, HRR prediction models were constructed using decision tree regression (DT), K-nearest neighbor regression (KNN), and linear regression (LR). The results indicate that the DT model performs best in HRR prediction (MAE = 0.4889, MSE = 0.7414, RMSE = 0.8571, R² = 0.9991), effectively capturing the nonlinear variation patterns in the HRR. The correlation analysis and regression analysis conducted in this study provide crucial data support for fire combustion characteristics research, fire risk assessment, and fire safety optimization. Furthermore, the findings provide crucial data support for research on fire combustion characteristics and data-driven fire risk assessment, which may serve as a foundation for future AI-based real-time fire detection applications. Full article

A novel cost-effective, rapid, and eco-friendly method was described for the removal of carbon dioxide (CO₂) from the gaseous emissions of gasoline engines. This involved the use of a sandwich filter (~10 cm diameter) made of a nonwoven poly (m-phenylene isophthalamide) (Nomex) fabric loaded with a thin layer of activated carbon. The optimized filter, with an activated carbon mass of 2.89 mg/cm², a thickness of 4.8 mm, and an air permeability of 0.5 cm³/cm²/s, was tested. A simple homemade sampling device equipped with solid-state electrochemical sensors to monitor the concentration levels of CO₂ before and after filtration of the emissions was utilized. The data were transmitted via a General Packet Radio Service (GPRS) link to an Internet of Things (IoT)-based gas monitoring system for remote management, and real-time data visualization. The proposed device achieved a 70 ± 3.4% CO₂-removal efficiency within 7 min of operation. Characterization of the filter was conducted using a high-resolution scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX) and Brunauer–Emmett–Teller (BET) analysis. The effects of loaded activated carbon mass, fabric type, filter porosity, gaseous removal time, and adsorption kinetics were also examined. The proposed filter displayed several advantages, including simplicity, compactness, dry design, ease of regeneration, scalability, durability, low cost, and good efficiency. Heat resistance, fire retardancy, mechanical stability, and the ability to remove other gasoline combustion products such as CO, SO_x, NO_x, VOCs, and particulates were also offered. The filtration system enabled both in situ and on-line CO₂ real-time continuous emission monitoring. Full article

The use of CFRP composite increased significantly since the last 40 years for aircraft structure. Unfortunately, such structures are subjected to significant damages if struck by lightning compared to metallic structure. This is mainly due to the low conductivity of this material, which cannot evacuate the current without high Joule heating. Lightning strike-induced damage in a composite laminate is composed of in-depth delamination, fibre breakage, and resin deterioration due to the surface explosion and the core current flow linked to interaction of the arc with the surface. But very rare previous studies dedicated to the analysis of damage as a direct effect of lightning have considered the spurious effect of the paint that always covers real aeronautic structures neither on the thermal nor the mechanical loads that are the root cause of these damages. We present in this paper a coupled non-destructive and destructive damage analysis to support the proposition of damage scenarios depending on the presence and thickness of the paint. The mechanical and thermal sources contribution in the global loading on the core damage is discussed, which confirms previous studies’ analysis and modelling and is in accordance with existing works in the literature. Full article

The optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) principle, implemented and validated on the integrated energy experimental platform. The experimental system simulates load generation and dissipation processes using a water tank, where hourly varying heating power output emulates the dynamic cooling loads of buildings. By regulating the chilled water system through different algorithms, the temperature tracking control performance and cooling supply regulation accuracy were rigorously validated. The control module was written in the Python 3.8 environment, and Niagara 4 software was used as an intermediate software to achieve data interaction and logical control with the laboratory system. The experimental results show that this algorithm can follow the hourly optimized parameters with a low overshoot in the short-term domain. Meanwhile, it can achieve the optimal control of cooling capacity and energy consumption in the long-term domain. Compared with the PID strategy, the temperature following control accuracy can be improved by 9.64%, and the cooling capacity can be saved by 6.24%. Compared with the day-ahead MPC algorithm, the temperature following control accuracy can be relatively improved by 16.52%, and the cooling capacity can be saved by 1.24%. Full article