Search Results (5,369)

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

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

In this paper, the dynamic modeling and integrated simulation of a ship microgrid system designed to enhance power quality and energy efficiency in electric propulsion vessels are proposed. The proposed system consists of a photovoltaic (PV) array, a battery energy storage system (BESS), a diesel generator, and a propulsion system, all of which are organically integrated through power conversion devices. To compensate for the intermittent nature of solar power, a control strategy featuring Maximum Power Point Tracking (MPPT) for the PV system and bidirectional DC/DC converter control for the battery was implemented. Specifically, a control logic to stabilize the system output in response to the fluctuating loads of the electric propulsion system was developed using PSCAD (v50) software. The simulation results demonstrate that the proposed control strategy maintains DC-link voltage deviation within ±1.8% and achieves a settling time of less than 0.8 s while optimizing propulsion efficiency (peak-shaving ratio 25–30%) under both constant and variable speed operating conditions. Battery SOC variation is limited to 18–88%, preventing overcharge or discharge. This research provides a foundational framework for the design of energy management systems (EMSs) and grid stability assessments for future eco-friendly electric propulsion ships. Full article

Shared e-scooter systems are frequently characterized as zero-emission mobility solutions; however, lifecycle greenhouse gas (GHG) emissions depend on manufacturing, electricity generation, and operational logistics. While conventional life cycle assessment (LCA) studies quantify environmental impacts using static average parameters, they rarely integrate lifecycle emissions into real-time fleet decision-making. This study proposes a formally defined carbon-aware operational framework that integrates ride-level telemetry, time-varying electricity grid carbon intensity, amortized production emissions, and dynamically allocated logistics impacts into a unified optimization architecture. Lifecycle emissions are computed at ride-level granularity and incorporated into charging and rebalancing decisions through a constrained optimization framework. A multi-objective extension is introduced to account for environmental–economic trade-offs. An illustrative simulation of 1000 rides was conducted to evaluate the operational performance of the framework. Under the assumed baseline scenario, the illustrative carbon-aware simulation indicated a potential reduction of up to 24.5% relative to conventional scheduling. Sensitivity analysis across variations in grid carbon intensity, scooter lifetime, energy consumption, and logistics emissions demonstrated reduction outcomes ranging between 18% and 29%, indicating robustness to parameter uncertainty. The study does not present large-scale empirical validation but provides a mathematically formalized decision-support architecture that operationalizes lifecycle assessment within shared micro-mobility fleet management. The results suggest that integrating carbon metrics into operational control may substantially enhance the environmental performance of shared e-scooter systems. Future research should validate the framework using real-world fleet data and incorporate a comprehensive economic assessment. The proposed framework provides a scalable methodological basis for integrating environmental metrics into real-time micro-mobility management and urban sustainability planning. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

In this work, a high-performance mid-wave infrared (MWIR) photodetector (PD) utilizing an InAs/GaSb Type-II superlattice absorber and a quaternary AlGaAsSb barrier is designed and analyzed based on numerical simulations aimed at determining an optimized detector structure. Through these simulations, the composition of the AlGaAsSb barrier is carefully designed to achieve lattice matching, high conduction band offset and zero valence band offset. By optimizing the barrier thickness and doping concentration, the depletion region is effectively shifted from the narrow-bandgap absorber to the wide-bandgap barrier; additionally, at 150 K and a reversed bias of 0.05 V, the dark current density in the PD with the barrier (pBn) is reduced to 1.83 × 10⁻⁵ A/cm², about two orders of magnitude lower than that of the PD without the barrier. Furthermore, the effect of the barrier on the generation–recombination (G-R) and the trap-assisted tunneling (TAT) currents are analyzed and compared in detail, and it is found that the barrier structure is much more effective in suppressing the TAT current at low reversed bias when the electric field is low in the absorber layer. These results demonstrate the efficacy of the proposed AlGaAsSb barrier design for realizing high-operating-temperature MWIR PDs. It also provides an insight into the physical mechanism that leads to the performance enhancement of InAs/GaSb PDs. Full article

Upon the challenges of climate change and the demand for energy sustainability, nuclear power (NP) units not only provide clean electricity but are also equipped for cogeneration to achieve energy cascade utilization; this represents a key avenue for improving the overall efficiency and achieving the comprehensive utilization of nuclear energy. However, following the heating retrofitting stage, there exists a risk that the supply control valve of the unit may accidentally open completely during operation, which increases the risk of over-powering. Therefore, this study designs response schemes for second-generation large pressurized water reactor NP plants (NPPs) under the accidental full-open condition of the heat-supply control valve. Specifically, an integrated model encompassing the nuclear steam supply system, secondary circuit system, thermal energy supply system (TESS), and related control systems was constructed using the optimal estimation program and 3KeyMaster simulation platform. Subsequently, two response schemes were designed for the accidental full-open valve scenario under two operation modes—namely, the “Reactor Follows Turbine + TESS” and “Turbine Follows TESS” modes. Finally, on the basis of the established simulation platform, the scenario of accidental full opening of the heat-supply control valve was simulated and verified. Ultimately, the results indicate that the response scheme implemented under the “Turbine Follows TESS” mode is more effective in suppressing nuclear overpower when the heat supply control valve accidentally opens fully. Thus, overall, this study provides a feasible accident response strategy and critical technical reference for NPPs involving cogeneration and energy cascade utilization. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

The remaining useful life (RUL) prediction of lithium-ion batteries remains challenging for hybrid models due to high computational redundancy and hyperparameter sensitivity under complex operating conditions. To address these issues, this paper proposes a novel hybrid framework that integrates a CNN–BiLSTM–Attention network with a Crested Porcupine Optimizer (CPO). The key innovation lies in the simultaneous co-optimization of model structure and parameters: a group-level structured pruning strategy eliminates redundant convolutional kernels to reduce complexity, while the CPO algorithm dynamically optimizes critical hyperparameters (e.g., BiLSTM hidden nodes, attention dimensions) using RMSE as the fitness function. This dual optimization achieves a balance between model lightweighting and predictive accuracy. Experimental results on real-world electric vehicle datasets demonstrate that the optimized model reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 60.7% and 55.2%, respectively, compared to the baseline CNN–BiLSTM–Attention model. Furthermore, the model maintains high robustness across multiple datasets (R² > 0.996), validating its strong generalization capability in small-sample and high-noise scenarios. Full article

Cogeneration system optimization in refineries confronts the challenge of simultaneously integrating design parameter selection and topological configuration. The literature typically addresses these aspects separately: parametric optimization with fixed topology or configuration optimization for specific nominal conditions. This work develops a comprehensive methodology integrating exhaustive parametric exploration with superstructure-based optimization through mixed-integer nonlinear programming (MINLP), applied to the Miguel Hidalgo refinery in Tula, Mexico. The systematic procedure generates superstructures considering all viable expansion and tempering routes under steam quality restrictions ($x\ge 0.88$), evaluating 84–105 combinations of generation pressure ($P_{\mathrm{HRSG}}=70$–140 bar) and superheater outlet temperature ($T_{s_4}=500$–560 °C). The analysis reveals three topologically distinct configurations identified as generating maximum power under different operating conditions and characterizes how transitions between high-performing configurations occur at discrete thermodynamic thresholds that correlate with constraint activation contradicting the conventional assumption of continuous parameter-configuration relationships. Multi-criteria evaluation positions Configuration 1 as the recommended design, generating 25% increase in electric generation, 11% improvement in utilization factor ($UF$: $0.640\to 0.710$) and 20% reduction in specific fuel consumption ($SFC$: $0.259\to 0.207$ kg/kWh). The methodology is directly generalizable to other refineries through universal thermodynamic principles, with a systematic five-step procedure applicable to any multi-pressure steam demand profile. The characterization of discrete transition phenomena and the associated methodology for their thermodynamic explanation challenges the conventional assumption of continuous parameter–configuration relationships in optimization approaches, with immediate implications for the design of flexible cogeneration systems in refineries worldwide. Full article

Many countries rely on coal for energy security during renewable transitions. This study conducts a technical, economic, and environmental analysis of hybridizing a supercritical coal-fired power unit with photovoltaics (PV) to create a sustainable hybrid system at a plant in Silesian Voivodeship, Poland. The goal is to assess costs and optimal operating conditions for a coal–PV hybrid under varying scenarios, using a decision-support model that integrates fuel prices, CO₂ emission charges (EUA), and technical parameters. Two main scenarios are modeled. In auxiliary-only PV (112 MW system), real-time power supplies pumps and fans, cutting coal consumption without storage; LCOE decreases with annual hours (2800–7000), outperforming conventional coal across EUA prices (20–50 EUR/t). In PV surplus export, excess generation (1300 h/year) is grid-fed for revenue, amplifying LCOE reductions—hybrid superiority emerges above 34 EUR/t EUA, per equivalence thresholds. Results show coal electricity exceeds low-emission costs above 34 EUR/t CO₂, with maximum disparity at 50 EUR/Mg. The hybrid leverages existing infrastructure, mitigates solar intermittency via auxiliary supply, ensures baseload continuity, boosts flexibility, and prolongs asset life—reducing >123,000 EUA/year at 145,000 MWh PV output. This sustainable hybrid promotes energy transition, reduces fossil fuel dependence, and aligns with global sustainability goals. Full article

Climate change poses a growing threat to global health, yet healthcare systems contribute substantially to environmental harm through energy use, waste, and greenhouse gas (GHG) emissions. Among hospital departments, Intensive Care Units (ICUs) are among the most resource- and energy-intensive, generating disproportionately high greenhouse gas (GHG) emissions. The aim of this systematic review is to synthesize the literature on the environmental footprint of ICUs and to develop evidence-based strategies for creating sustainable ‘Green ICUs’ in accordance with the PRISMA 2020 guidelines. Peer-reviewed studies published between 2012 and October 2025 were identified through searches of major biomedical databases. Eligible studies examined the impacts of climate change on human health and infectious diseases, the ecological footprint of medical imaging and personal protective equipment, and sustainability interventions relevant to adult intensive care units. The environmental footprint of ICUs ranges from 88 to 178 kg CO₂-equivalents per patient per day. High electricity consumption, especially from heating, ventilation, and air-conditioning (HVAC) systems, along with single-use medical supplies and diagnostic imaging, drives this impact. Life-cycle assessments consistently demonstrate that reusable textiles, optimized energy systems, and rationalized diagnostic practices significantly reduce emissions and waste. Educational and behavioral interventions were effective in reducing unnecessary consumable use while maintaining patient safety. A “Green ICU” model integrating energy efficiency, sustainable procurement, waste reduction, and staff education can substantially reduce environmental harm without compromising quality of care. Full article

Microbiological contamination of drinking water remains a significant public health concern worldwide, necessitating the development of efficient and environmentally friendly disinfection technologies. This study investigated the effectiveness and physicochemical mechanisms of water treatment using high-frequency electrical discharge plasma. Experimental research was conducted employing a laboratory dielectric barrier discharge reactor operating at 10–30 kHz and 10–25 kV, with treatment durations ranging from 5 to 20 min. Plasma exposure resulted in pronounced physicochemical changes in the aqueous medium, including a decrease in pH from 7.1–7.3 to 5.4–6.0 and an increase in electrical conductivity from 280–340 µS/cm to 480–620 µS/cm. The formation of reactive oxygen species, including hydroxyl radicals, ozone, and hydrogen peroxide, was confirmed, with hydrogen peroxide concentrations varying between 0.35 and 1.20 mg/L. Microbiological analysis demonstrated a reduction in microbial concentration from approximately 10⁵–10⁶ CFU/mL to 10²–10³ CFU/mL, corresponding to 3–4 log inactivation. The results indicated that microbial reduction was strongly associated with the generation of reactive species and treatment duration. Energy density within the range of 0.3–1.2 kWh/m³ was found to support effective disinfection performance. The findings demonstrated that high-frequency plasma treatment established a strong oxidative environment leading to microbial membrane disruption and cellular damage. Overall, the study confirmed the potential of high-frequency electrical discharge plasma technology as a promising approach for drinking water disinfection and provided a basis for further optimization and scale-up investigations. Full article

To address the low efficiency of finite element modeling and the reliance on manual measurements in electric/magnetic field analysis of complex overhead transmission line structures, this paper develops an IronPython-based automated computational platform within ANSYS Maxwell for 3-D modeling and electric/magnetic field analysis. First, by parsing transmission line data from the Grid Information Model (GIM), a unified coordinate transformation method is proposed to convert geographical coordinates into three-dimensional (3-D) Cartesian coordinates for finite element analysis. Based on the extracted line parameters, conductor sag is calculated and catenary modeling is implemented. An equivalent radius method is also introduced to simplify multi-bundle conductor modeling, enabling fast parametric construction of complex 3-D transmission line models. Second, by combining the IronPython scripting language with the .NET Windows Forms control library, a visualized finite element modeling and computation platform is developed. Finally, a typical double-circuit transmission line on the same tower is taken as a case study to calculate the spatial distribution of electric/magnetic fields. The influence of solution domain size on electric/magnetic field computation results is investigated, and optimal solution domain parameters are determined. The finite element results generated by the developed platform are further validated through comparison with measured data. The results demonstrate good agreement between calculated and measured values, confirming the accuracy and engineering applicability of the developed platform for electric/magnetic environment analysis of overhead transmission lines. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article