Search Results (66)

The UK faces an urgent challenge to simultaneously accelerate housing delivery and reduce whole-life carbon emissions, yet robust empirical evidence on the carbon performance of modular steel housing remains limited. This study aims to quantify the carbon impacts of a modular light-gauge steel frame social housing dwelling in the UK and to benchmark its performance against contemporary low-carbon construction typologies. A cradle-to-grave life cycle assessment was conducted using primary project data from a real modular housing development, with embodied carbon modelled in One Click LCA and operational energy assessed through SAP 10.2-verified datasets. The results indicate a total whole-life carbon footprint of 91.3 tCO₂e over a 50-year period, with embodied emissions (A1–A3) accounting for 38.2% and operational energy and water use contributing 48.1%. The normalised embodied carbon intensity of 366 kgCO₂e/m² (A1–A5) is comparable to recent high-performing cross-laminated timber buildings, demonstrating that optimised modular steel systems can allow for low-carbon outcomes typically associated with bio-based construction. Sensitivity analysis shows that low-carbon foundation concrete, bio-based insulation, and steel optimisation can reduce upfront emissions by approximately 8–10%. Dynamic energy simulations were also used to assess how different design choices influence operational carbon emissions. This study provides transparent, real-project evidence of the whole-life carbon performance of UK modular light-gauge steel frame housing and identifies practical design strategies for further decarbonisation. The findings support informed decision-making for policymakers, designers, and housing providers seeking scalable, low-carbon residential solutions. Full article

The range-extended sprayer can effectively balance the requirements of economy and power performance, which represents the development and transformation trend of intelligent plant protection machinery in the future. To more intuitively and reliably explore the energy variation rules of the range-extended sprayer under different energy management strategies (EMSs) and achieve optimal fuel economy, a co-simulation platform for energy management of the range-extended sprayer under multi-condition cyclic operation was established based on AVL-CRUISE and MATLAB Simulink. Meanwhile, a fuzzy control-based EMS optimized by the particle swarm optimization (PSO) algorithm was proposed. Simulation results show that the comprehensive fuel consumption of the PSO-optimized fuzzy control EMS is 3.68 kg; compared with the conventional fuzzy control strategy, its fuel economy is improved by 4.90%, and by 8.23% compared with the multi-point power following strategy. Subsequently, an energy management test platform for the range-extended sprayer was built, and experimental verification was carried out. The platform test results indicate that the electricity difference between the platform test and the simulation test is 0.38%, and the fuel consumption difference is 1.6%, both within a reasonable range. This further verifies the reliability of the simulation platform for the improved energy management model and the feasibility of the proposed EMSs. The research content and results provide theoretical basis and technical support for the optimization of EMSs and the joint simulation method of energy management for range-extended sprayers. Full article

To address the stringent emission regulations of the International Maritime Organization (IMO) and the growing demand for green port operations, this study proposes an innovative range-extended series hybrid powertrain system featuring a dedicated methanol engine as an Auxiliary Power Unit (APU) for harbor tugs. Based on an analysis of actual ship operational data, a core design paradigm of “battery-dominant, engine-as-range-extender” is established. A robust capacity parameter matching method is proposed, yielding a configuration comprising a 200 kW∙h/600 kW Lithium Iron Phosphate Battery Pack (LFPBP), a 250 kW methanol APU, and a 400/600 kW Permanent Magnet Synchronous Propulsion Motor (PMSM). A hierarchical intelligent energy management strategy (EMS), integrating state-machine coordination and real-time power allocation, is designed. High-fidelity simulations under a typical duty cycle demonstrate that the proposed system achieves an equivalent fuel-saving rate of 50.8% compared with a conventional diesel system, with the engine operating exclusively in its high-efficiency zone (>42% efficiency) for only 35% of the operational time. A full life-cycle techno-economic analysis reveals an incremental investment payback period (PBP) of approximately 3 months and a net present value (NPV) exceeding USD 9.69 million over a 10-year period. Quantitative environmental analysis shows an annual reduction of approximately 94.8% in CO₂ emissions (assuming the use of green methanol produced from renewable sources and captured CO₂), 95% in NO_x emissions, and the near-elimination of SO_x and particulate matter (PM). This study provides a systematic and economically attractive solution with promising engineering feasibility verified by simulation, which paves the way for further experimental validation and practical engineering implementation. Full article

Hydrogen fuel cell tractors (HFCTs) represent a critical frontier in the development of modern green agricultural equipment. Due to the heavy-duty and highly variable nature of tractor operations, current fuel cell-powered platforms face significant challenges, including insufficient energy sustainability and low-efficiency consumption. This study addresses the issues of sluggish dynamic response and durability degradation during complex plowing tasks through systematic power system modeling and energy management strategy (EMS) research. First, a control-oriented fuel cell model coupling mechanical inertia, manifold filling-and-emptying dynamics, and electrochemical reactions is established, which quantitatively reveals the physical boundaries of load-change ramp rates. On this basis, a multi-dimensional performance evaluation framework for HFCTs is constructed. This framework innovatively proposes fuel cell dynamic response indicators and a non-linear calculation model for continuous operational duration, achieving a non-linear mapping between onboard energy storage capacity and operating time for quantitative endurance assessment. Subsequently, guided by this evaluation system, a dynamic program considering the coordination of energy system durability and the energy consumption economy (DP-CoDE) is developed. By establishing an online update mechanism for power-change rates, synergistic optimization of system durability and economy is achieved based on the DP-CoDE strategy. Model-in-the-loop simulation results under plowing conditions demonstrate that, compared to the DP-CoDE strategy, the proposed strategy enhances response stability by 44.44% and reduces response tracking error by 41.17% at a marginal cost of only a 0.15% increase in total hydrogen consumption. These findings significantly improve the system’s tracking capability under transient complex loads and provide a robust theoretical foundation for the control system design of HFCTs. Full article

The article presents an attempt by the authors to generate a digital representation of the analyzed surface layer of WC-Co-Al₂O₃ coating deposited by the ESD method. The WC-Co-Al₂O₃ surface layer is superhard and abrasion-resistant, significantly increasing the exploitation time of working elements. The authors aim to develop a method for generating series of digital surfaces with similar geometry parameters based on data collected through profilometric analysis. Therefore, the advanced integration of machine learning (ML) techniques with classical statistical approaches for modeling and predicting stochastic processes. While traditional models such as ARMA/ARIMA and hidden Markov models (HMMs) offer mathematical rigor, they often impose assumptions of stationarity and linearity, which limits their application to complex, noisy data. This paper proposes a model for surface geometry generation based on experimental data that combines recurrent neural networks (RNNs) and Monte Carlo simulation. Additionally, the study reviews emerging methods, including generative adversarial networks (GANs) for stochastic simulation and expectation-maximization (EM) algorithms for parameter estimation. An empirical case study on WC-Co-AL₂O₃ surface geometries demonstrates the effectiveness of ML–stochastic hybrids in capturing both deterministic structures and random fluctuations. The findings underscore not only the benefits but also the limitations of such models, including high computational demands and interpretability challenges, while proposing future research directions toward physics-informed ML and explainable AI. Full article

In this study, we present the design and analysis of a stacked inverter-based millimeter-wave (mmWave) power amplifier (PA) in 90 nm CMOS-targeting wideband Q-band operation. The PA employs two PMOS and two NMOS devices in a fully stacked inverter topology to distribute device stress, remove the need for an RF choke, and increase effective transconductance while preserving compact layout. A resistor ladder biases the stack near $V_{\mathrm{DD}}/4$ per device, and capacitive division steers intermediate-node swings to enable class-E-like voltage shaping at the output. Closed-form models are developed for gain, output power, drain efficiency/PAE, and linearity, alongside a small-signal stacked-ladder formulation that quantifies stress sharing and the impedance presented to the matching networks; L/T network synthesis relations are provided to co-optimize bandwidth and insertion loss. Post-layout simulation in 90 nm CMOS shows $|S_{21}|$ = 10 dB at 39.84 GHz with 3 dB bandwidth from 36.8 to 42.4 GHz, peak PAE of 18.38% near 41 GHz, and saturated output power $P_{\mathrm{sat}}=8.67$ dBm at $V_{\mathrm{DD}}=4$ V, with $S_{11}<-15$ dB and reverse isolation $\approx -16$ dB. The layout occupies $1.6\times 1.6$ mm² and draws 31.08 mW. Robustness is validated via a 200-run Monte Carlo showing tight clustering of $P_{\mathrm{sat}}$ and PAE, sensitivity sweeps identifying sizing/tolerance trade-offs ($\pm 10\%$ devices/passives), and EM co-simulation of on-chip passives indicating only minor loss/shift relative to schematic while preserving the target bandwidth and efficiency. The results demonstrate a balanced gain–efficiency–power trade-off with layout-aware resilience, positioning stacked-inverter CMOS PAs as a power- and area-efficient solution for mmWave front-ends. Full article

This paper presents an economic–environmental power dispatch approach for a grid-connected microgrid (MG) with photovoltaic (PV) generation and battery energy storage systems (BESSs). The problem was formulated as a multiobjective optimization problem with functions such as minimizing fixed and variable generation costs, power losses, and CO₂ emissions. This study addresses the problem of intelligent energy management in microgrids with PV generation and BESSs to optimize their performance based on multiple criteria. This study focuses on optimizing the Energy Management System (EMS) with metaheuristic algorithms to achieve practical implementation with simpler algorithms to solve a complex optimization problem. This study employs four multiobjective optimization algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II), Harris Hawks Optimization (HHO), multiverse optimizer (MVO), and Salp Swarm Algorithm (SSA), which are classified as robust techniques for obtaining Pareto fronts. The computational resources employed to simulate the problem are presented. The optimal dispatch obtained from the Pareto front achieved reductions of 0.067% in fixed costs, 0.288% in variable costs, 3.930% in power losses, and 0.067% in CO₂ emissions, demonstrating the effectiveness of the proposed approach in optimizing both economic and environmental performance. The SSA stood out for its stability and computational efficiency, establishing itself as a promising method for energy management in urban and rural microgrids (MGs) and providing a solid framework for optimization in alternating current systems. Full article

Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive thermal effect modeling and thermal runaway fault diagnosis, leading to irreversible aging and thermal runaway risks for LIBs and PEMFCs stacks under complex operating conditions. To address this challenge, this paper proposes a thermo-electrical co-optimization EMS incorporating thermal runaway fault diagnosis actuators, with the following innovations: firstly, a dual-layer framework integrates a temperature fault diagnosis-based penalty into the EMS and a real-time power regulator to suppress heat generation and constrain LIBs/PEMFCs output, achieving hierarchical thermal management and improved safety; secondly, the distributional soft actor–critic (DSAC)-based EMS incorporates energy consumption, state-of-health (SoH) degradation, and temperature fault diagnosis-based constraints into a composite penalty function, which regularizes the reward shaping and guides the policy toward efficient and safe operation; finally, a thermal safe constriction controller (TSCC) is designed to continuously monitor the temperature of power sources and automatically activate when temperatures exceed the optimal operating range. It intelligently identifies optimized actions that not only meet target power demands but also comply with safety constraints. Simulation results demonstrate that compared to DDPG, TD3, and SAC baseline strategies, DSAC-EMS achieves maximum reductions of 39.91% in energy consumption and 29.38% in SoH degradation. With the TSCC implementation, enhanced thermal safety is achieved, while the maximum energy-saving improvement reaches 25.29% and the maximum reduction in SoH degradation attains 20.32%. Full article

Developing multi-microgrid (MMG) systems provides a new paradigm for power distribution systems with a higher degree of resilience, flexibility, and sustainability. The inclusion of communication networks as part of MMG is critical for coordinating distributed energy resources (DERs) in real time and deploying energy management systems (EMS) efficiently. However, the communication quality of service (QoS) parameters such as latency, jitter, packet loss, and throughput play an essential role in MMG control and stability, especially in highly dynamic and high-traffic situations. This paper presents a focused review of MMG systems from a cyber-physical viewpoint, particularly concerning the challenges and implications of communication network performance of energy management. The literature on MMG systems includes control strategies, models of communication infrastructure, cybersecurity challenges, and co-simulation platforms. We have identified research gaps, including, but not limited to, the need for scalable, real-time cyber-physical systems; limited research examining communication QoS under realistic conditions/traffic; and integrated cybersecurity strategies for MMGs. We suggest future research opportunities considering these research gaps to enhance the resiliency, adaptability, and sustainability of modern cyber-physical MMGs. Full article

To address the challenges of high fuel consumption and emissions in traditional diesel-powered inland law enforcement vessels, this study proposes a methanol–electric hybrid propulsion system retrofitted with a novel energy management strategy (EMS) based on the integration of Dynamic Programming (DP) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DP-ANFIS algorithm combines the global optimization capability of DP with the real-time adaptability of ANFIS to achieve efficient power distribution. A high-fidelity simulation model of the hybrid system was developed using methanol engine bench test data and integrated with models of other powertrain components. The DP algorithm was used offline to generate an optimal control sequence, which was then learned online by ANFIS to enable real-time energy allocation. Simulation results demonstrate that the DP-ANFIS strategy reduces total energy consumption by 78.53%, increases battery state of charge (SOC) by 3.24%, decreases methanol consumption by 64.95%, and significantly reduces emissions of CO, HC, NOx, and CO₂ compared to a rule-based strategy. Hardware-in-the-loop tests confirm the practical feasibility of the proposed approach, offering a promising solution for intelligent energy management in marine hybrid propulsion systems. Full article

This review comprehensively presents the development of energy-efficient pneumatic propulsion systems (PPSs) in road vehicle applications, which are classified as green vehicles. The advantages and disadvantages of PPSs were presented, and PPSs were compared with combustion propulsion systems (CPSs) and electric propulsion systems (EPSs), as well as their power-to-weight ratios (PWRs), energy densities, and CO₂ emissions. The review of compressed air vehicles (CAVs) focuses on their historical development and future prospects. This review discusses the use of PPSs with compressed air engines (CAEs) as an alternative propulsion system in green vehicles, providing a simple, energy-saving, and environmentally friendly solution. This review also discusses hybrid air propulsion, which, when combined with internal combustion engines (ICEs) or electric motors (EMs), offers innovative energy-efficient propulsion systems that are more economical than conventional hybrid propulsion systems. This review focuses on recent advances in lightweight air vehicles that improve vehicle handling, increase efficiency, and reduce propulsion energy consumption. Discussion of the study results concerns the use of PPSs in a three-wheeled rehabilitation tricycle (RTB). A comprehensive computation model of the RTB was presented, and the key performance parameters crucial to its operation were analyzed. The results of the RTB simulation were verified through field tests. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

An intuitive method for estimating the inertia contribution from residual sources, such as induction motors and inverter-based power electronic facilities, in the Korea Electric Power Corporation (KEPCO) system is proposed. First, the method utilizes synchronized Phasor Measurement Units (PMUs) to obtain the measured system Rate of Change of Frequency (RoCoF) following an instantaneous power imbalance. Subsequently, the estimated system RoCoF for the same event is derived from simulations of the full dynamic model of the KEPCO system using Energy Management System (EMS) data. The estimated RoCoF accounts only for the inertia contribution from synchronous generators, as the dynamic model includes only these generators. The residual inertia of the entire power system is then estimated based on the ratio of the estimated RoCoF to the measured RoCoF, using the known inertia contribution from synchronous generators. The effectiveness of the proposed method is validated through dynamic simulations of the KEPCO system and demonstrated using real PMU and EMS data from actual disturbance events. The results illustrate that residual inertia was estimated at approximately 160 GW during daytime and around 67 GW during nighttime, indicating substantial variation in absolute terms. This finding highlights the importance of considering residual inertia contributions, particularly under varying load conditions. Full article

Under the “dual-carbon” strategy, accurately quantifying carbon emissions in water conservancy projects is crucial to promoting low-carbon construction. However, existing life cycle assessment (LCA) methods for carbon emissions during the mechanical construction stage often fail to reflect actual processes and are limited by high costs and lengthy data collection, potentially leading to inaccurate estimates. To address these challenges, this paper proposes a carbon emission evaluation method for the mechanical construction stage, based on carbon footprint theory and discrete event simulation (DES). This method quantifies equipment operation time and energy consumption during the drilling and blasting processes, enabling a detailed and dynamic emission analysis. Using the Fumin Pumped Storage Power Station Tunnel Project as a case study, a comparative analysis is conducted to examine the carbon emission characteristics of drilling and blasting operations under different surrounding rock conditions based on DES. The validity of the proposed model is confirmed by comparing its results with monitoring data and LCA results. The results show a clear upward trend in carbon emission intensity as surrounding rock conditions deteriorate, with emission intensity rising from 8405.82 kgCO₂e/m for Class II to 16,189.30 kgCO₂e/m for Class V in the headrace tunnel. The total carbon emissions of the water conveyance tunnels reach 40,019.64 tCO₂e, with an average intensity of 13,565.98 kgCO₂e/m. This study presents a refined and validated framework for assessing the carbon emissions of pumped storage tunnels. It addresses key limitations of traditional LCA methods in the mechanical construction stage and provides a practical tool to support the green transition of hydraulic infrastructure. Full article