Search Results (16,123)

Ammonia is an ideal candidate for clean energy in the future, and its large-scale production has long relied on the Haber–Bosch process, which operates at a high temperature and pressure. However, this process faces significant challenges due to the growing demand for ammonia and the increasing need for environmental protection. The high energy consumption and substantial CO₂ emissions associated with the Haber–Bosch method have greatly limited its application. Consequently, increasing research efforts have been devoted to developing green ammonia synthesis technologies. Among these, the electrocatalytic nitrogen reduction reaction (NRR), which uses water and nitrogen as raw materials to synthesize NH₃ under mild conditions, has emerged as a promising alternative. This method offers the potential for carbon neutrality and decentralized production when coupled with renewable electricity. However, it is important to note that the current energy efficiency and ammonia production rates of NRR under ambient aqueous conditions generally lag behind those of modern Haber–Bosch processes integrated with green hydrogen (H₂). As the core of the NRR process, the performance of electrocatalysts directly impacts the efficiency, energy consumption, and product selectivity of the entire reaction. To date, significant efforts have been made to identify the most suitable electrocatalysts. In this paper, we focus on the current research status of metal catalysts—including both precious and non-precious metals—as well as non-metal catalysts. We systematically review important advances in performance optimization, innovative design strategies, and mechanistic analyses of various catalysts. We clarify innovative optimization strategies for different catalysts and summarize and compare the catalytic effects of various catalyst types. Finally, we discuss the challenges facing electrocatalysis research and propose possible future development directions. Through this paper, we aim to provide guidance for the preparation of high-efficiency NRR catalysts and the future industrial application of electrochemical ammonia synthesis. Full article

To address the problem of selecting an “appropriate” charging station for emergency charging during the journey of electric vehicles, this paper proposes a basic architecture of an intelligent charging navigation system composed of the power system, traffic system, charging stations, and on-board navigation terminals. The concept of a charging time window is introduced into a “reservation-based charging + consumption” service model for electric vehicle charging prediction. On this basis, a dynamic dispatching model based on a rolling time axis is designed, enabling the charging process of users to be freed from the constraints of queuing time and time-dependent charging service fees. Case simulations show that intelligent charging navigation for electric vehicles based on reservation charging service can effectively improve the users’ charging experience while taking into account both the operating state of the power grid and the benefits of charging station operators. Full article

There is a lack of systematic research on the behavioral design of charging decision-making for Shared Autonomous Electric Vehicles (ASEVs), and the thresholds of “when to charge and where to charge” have not been clarified. Therefore, this paper investigates the optimization of charging decisions of SAEVs and the impact of different decision-making objectives to provide theoretical support and practical guidance for intelligent operation. A multi-agent simulation model (which accurately simulates complex interaction systems) is constructed to simulate the operation and charging behavior of SAEVs. Four charging decision optimization objective functions are defined, and a weighted multi-objective optimization method is adopted. A comprehensive solution process combining the multi-agent simulation model and genetic algorithm (efficiently solving complex objective optimization problems) is applied to approximate the global optimal solution among 35 scenarios through 100 iterative runs. In this paper, factors such as passenger demand (e.g., average remaining battery power, demand response time) and operator demand (e.g., empty vehicle mileage, charging cost) are considered, and the impacts of different objectives and decision variables are analyzed. The optimization results show that (1) when a single optimization objective is selected, minimizing the total charging cost effectively balances the overall fleet operation; (2) there are trade-offs between different objectives, such as the conflict between the remaining battery power and charging cost, and the balance between the demand response time and the empty vehicle mileage; and (3) in order to satisfy the operational requirements, the weight distribution, charging probability, stopping probability, and recommended battery power should be adjusted. In conclusion, this study provides optimal charging decision strategies for the intelligent operation of SAEVs in different scenarios, which can optimize target weights and charging parameters, and achieve dynamic, balanced fleet management. Full article

This study proposes a compensation method for operating region variations in in-wheel PMSMs, which are widely used in small mobility applications such as e-scooters and e-bikes. As motor temperature increases during operation, electrical parameters such as inductance vary, leading to unstable control. To address this, a Single-Layer Backpropagation Neural Network (SLBPNN) is used to estimate inductance variations in real-time. The proposed algorithm adjusts the motor’s operating point to maintain stable performance under thermal stress. Simulation results using MATLAB 2024b confirm the model’s effectiveness by estimating inductance from voltage, current, speed, and position inputs. Experimental validation further demonstrates that the proposed method compensates for the shift in the operating region due to temperature changes, improving the overall motor efficiency. Full article

Global warming and the energy crisis are two significant challenges in the world. The prime sources of greenhouse gas emissions are the transportation and power generation sectors because they rely on fossil fuels. To overcome these problems, the world needs to adopt electric vehicles (EVs) and renewable energy sources (RESs) as sustainable solutions. The rapid evolution of electric mobility is largely driven by the development of EV charging infrastructures (EVCIs), which provide the essential support for large-scale EV adoption. As the number of CIs grows, the utility grid faces more challenges, such as power quality issues, power demand, voltage instability, etc. These issues affect the grid performance, along with the battery lifecycle of the EVs and the charging system. A charging infrastructure integrated with the RES-based microgrid (MG) is an effective way to moderate the problem. Also, these methods are about reframing how smart sustainable cities generate, distribute, and consume energy. MG-based CI operates on-grid and off-grid based on the charging demand and trades electricity with the utility grid when required. This paper presents state-of-the-art transportation electrification, MG classification, and various energy sources in the DC MG. The grid-integrated DC MG, international standards for EV integration with the grid, impacts of CI on the electrical network, and potential methods to curtail the negative impact of EVs on the utility grid are explored comprehensively. The negative impact of EV load on the voltage profile and power loss of the IEEE 33 bus system is analysed in three diverse cases. This paper also provides directions for further research on grid-integrated DC MG-based charging infrastructure. Full article

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

Rising carbon emissions from international road freight transport in the EU—increasing from 29.4% in 2023 to 31.4% in 2025 under the With Existing Measures (WEM) Road Transport scenario—necessitate the implementation of additional measures within the framework of the EU Carbon Border Adjustment Mechanism (CBAM). For Ukraine, operating under martial law and pursuing a post-war green recovery of its transport and trade sectors, the adoption of EU experience in distributed generation (DG) from renewable energy sources (RESs) is particularly critical. This study evaluates the synergy between energy-efficient and low-carbon management in logistics chains for road freight transportation in Ukraine, drawing on EU evidence of DG based on RESs. To this end, a decoupling analysis was conducted to identify the factors influencing low-carbon and energy-efficient management of logistics chains in Ukraine’s freight transport sector. Under wartime conditions, the EU practice of utilising electric vehicles (EVs) as an auxiliary source of renewable energy for distributed electricity generation within microgrids—through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies—was modelled. The results confirm the relevance of RES-based DG and the integration of EVs as a means of enhancing energy resilience in resource-constrained and conflict-affected regions. The scientific novelty of this research lies in identifying the conditions for achieving energy-efficient and low-carbon effects in the design of logistics chains through RES-based distributed generation, grounded in circular and inclusive economic development. The practical significance of the findings lies in formulating a replicable model for diversifying low-carbon fuel sources via the development of distributed generation of electricity based on renewable resources, providing a scalable paradigm for energy-limited and conflict-affected areas. Future research should focus on developing innovative logistics chain models that integrate DG and renewable energy use into Ukraine’s transport system. Full article

Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. First, an operation and trading framework for distribution networks involving SS-VPPs is proposed. This framework comprehensively considers the clearing process of the electricity energy market, the operation mechanism of the distribution network, and the cost structures of various stakeholders, while clarifying the day-ahead market clearing mechanism at the distribution network level. Next, accounting for energy balance constraints and distribution network congestion constraints, this paper establishes a collaborative optimization model between SS-VPPs and active distribution networks. After obtaining the energy optimization results for all stakeholders, distribution locational marginal pricing (DLMP) is determined based on the dual problem solution to achieve multi-stakeholder market clearing. Finally, simulations using a modified IEEE 33-node test system demonstrate the rationality and feasibility of the proposed strategy. The framework fully exploits the market characteristics and dispatch potential of SS-VPPs, significantly reduces overall system operating costs, and ensures the economic benefits of all participants. Full article

In response to the limitations of traditional single-signal approaches, which fail to comprehensively reflect fault conditions, and the difficulties of existing feature extraction methods in capturing subtle fault patterns in transformer fault diagnosis, this paper proposes an innovative fault diagnosis methodology. Initially, to address common severe faults in excitation transformers, Principal Component Analysis (PCA) is applied to reduce the dimensionality of multi-source feature data, effectively eliminating redundant information. Subsequently, to mitigate the impact of non-stationary noise interference in voiceprint signals, a Deep Belief Network (DBN) optimized using the Hunter–Prey Optimization (HPO) algorithm is employed to automatically extract deep features highly correlated with faults, thus enabling the detection of complex, subtle fault patterns. For temperature and electrical parameter signals, which contain abundant time-domain information, the Random Forest algorithm is utilized to evaluate and select the most relevant time-domain statistics. Nonlinear dimensionality reduction is then performed using an autoencoder to further reduce redundant features. Finally, a multi-classifier model based on Adaptive Boosting with Support Vector Machine (Adaboost-SVM) is constructed to fuse multi-source heterogeneous information. By incorporating a pseudo-label self-training strategy and integrating a working condition awareness mechanism, the model effectively analyzes feature distribution differences across varying operational conditions, selecting potential unseen condition samples for training. This approach enhances the model’s adaptability and stability, enabling real-time fault diagnosis. Experimental results demonstrate that the proposed method achieves an overall accuracy of 96.89% in excitation transformer fault diagnosis, outperforming traditional models such as SVM, Extreme Gradient Boosting with Support Vector Machine (XGBoost-SVM), and Convolutional Neural Network (CNN). The method proves to be highly practical and generalizable, significantly improving fault diagnosis accuracy. Full article

This study presents the design and techno-economic optimization of a hybrid renewable energy system (HRES) for a coastal hotel in Manavgat, Türkiye. The system integrates photovoltaic (PV) panels, wind turbines (WT), pumped hydro storage (PHS), hydrogen storage (electrolyzer, tank, and fuel cell), batteries, a fuel cell-based combined heat and power (CHP) unit, and a boiler to meet both electrical and thermal demands. Within this broader optimization framework, six optimal configurations emerged, representing grid-connected and standalone operation modes. Optimization was performed in HOMER Pro to minimize net present cost (NPC) under strict reliability (0% unmet load) and renewable energy fraction (REF > 75%) constraints. The grid-connected PHS–PV–WT configuration achieved the lowest NPC ($1.33 million) and COE ($0.153/kWh), with a renewable fraction of ~96% and limited excess generation (~21%). Off-grid PHS-based and PHS–hydrogen configurations showed competitive performance with slightly higher costs. Hydrogen integration additionally provides complementary storage pathways, coordinated operation, waste heat utilization, and redundancy under component unavailability. Battery-only systems without PHS or hydrogen storage resulted in 37–39% higher capital costs and ~53% higher COE, confirming the economic advantage of long-duration PHS. Sensitivity analyses indicate that real discount rate variations notably affect NPC and COE, particularly for battery-only systems. Component cost sensitivity highlights PV and WT as dominant cost drivers, while PHS stabilizes system economics and the hydrogen subsystem contributes minimally due to its small scale. Overall, these results confirm the techno-economic and environmental benefits of combining seawater-based PHS with optional hydrogen and battery storage for sustainable hotel-scale applications. Full article

Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy, integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems, owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper proposes an operational optimisation and carbon reduction capability assessment framework for EH-ESs, focusing on revealing their operational response mechanisms and emission reduction potential under multi-disturbance conditions. A comprehensive model encompassing an electrolyser (EL), a fuel cell (FC), hydrogen storage tanks, and battery energy storage was constructed. Three optimisation objectives—cost minimisation, carbon emission minimisation, and energy loss minimisation—were introduced to systematically characterise the trade-offs between economic viability, environmental performance, and energy efficiency. Case study validation demonstrates the proposed model’s strong adaptability and robustness across varying output and load conditions. EL and FC efficiencies and costs emerge as critical bottlenecks influencing system carbon emissions and overall expenditure. Further analysis reveals that direct hydrogen utilisation outperforms the ‘electricity–hydrogen–electricity’ cycle in carbon reduction, providing data support and methodological foundations for low-carbon optimisation and widespread adoption of electricity–hydrogen 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

Potato peels are a waste product accounting for 15–40% of the mass of raw potatoes, depending on the processing method employed. The production of solid biofuel from potato peel was investigated in a superheated-steam fluidized bed filled with olivine sand. The co-fluidization of dried, crushed potato peels together with olivine sand was also investigated. Stable co-fluidization of olivine sand and crushed potato peels can be achieved when the mass fraction of potato peels in the fluidized bed does not exceed 3% (w/w). In a fluidized bed containing 3% % (w/w) potato peel, increasing the operational temperature of torrefaction from 200 to 300 °C with a processing duration of 30 min resulted in a 1.35-fold increase in HHV from 20.68 MJ/kg up to 27.93 MJ/kg based on ash-free dry mass. The effects of torrefaction temperature and duration on 5-hydroxymethylfurfural and furfural contents in condensable gaseous torrefaction products were studied, along with changes in the chemical composition of potato peel ash as a result of torrefaction. Furthermore, we analyzed the bed agglomeration index (BAI) predicting the possibility of agglomerate formation during combustion of torrefied potato peel in a fluidized bed and found that the probability of agglomeration may decrease along with increasing temperature and duration of the torrefaction process. Nevertheless, only the most severe torrefaction conditions of 300 °C for 30 min may completely prevent the risk of agglomerate formation during the subsequent combustion of torrefied potato peels as a solid biofuel. The proposed potato peel processing technology may be used in future frozen and fried potato factories in order to solve waste disposal issues while also reducing the costs of heat and electricity generation, as well as allowing for the recovery of high-value biochemicals from the torrefaction condensate. Full article

Crude oil emulsions continue to pose significant challenges across production, transportation, and refining due to their inherent stability and complex interfacial chemistry. Their persistence is driven by the synergistic effects of asphaltenes, resins, acids, waxes, and fine solids, as well as operational factors such as temperature, pH, shear, and droplet size. These emulsions increase viscosity, accelerate corrosion, hinder catalytic activity, and complicate downstream processing, resulting in substantial operational, economic, and environmental impacts—underscoring the necessity of effective demulsification strategies. This review provides a comprehensive examination of emulsion behavior, beginning with their formation, classification, and stabilization mechanisms and progressing to the fundamental processes governing destabilization, including flocculation, coalescence, Ostwald ripening, creaming, and sedimentation. Separation techniques are critically assessed across chemical, thermal, mechanical, electrical, membrane-based, ultrasonic, and biological domains, with attention to their efficiency, limitations, and suitability for industrial deployment. Particular emphasis is placed on hybrid and emerging methods that integrate multiple mechanisms to improve performance while reducing environmental impact. By uniting fundamental insights with technological innovations, this work highlights current progress and identifies future directions toward greener, more efficient oil–water separation strategies tailored to diverse petroleum operations. Full article

To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot interaction provides a valuable degree of flexibility in the process workflow. However, human behavior is characterized by unpredictable timing and variable task durations, which add considerable complexity to process planning. Therefore, it is crucial to develop a robust strategy for coordinating human and robotic tasks to manage the scheduling of production activities efficiently. This study proposes a global optimization approach to the scheduling of production activities, which employs a genetic algorithm with the objective of minimizing the total production time while simultaneously reducing the idle time of both the human operator and robot. The proposed approach is concerned with optimizing the sequencing of disassembly tasks, considering both temporal and exclusion constraints, to guarantee that tasks deemed hazardous are not executed in the presence of a human. This approach is based on a two-level adaptation framework developed in RoboDK (Robot Development Kit, v5.4.3.22231, 2022, RoboDK Inc., Montréal, QC Canada). At the first level, offline optimization is performed using a genetic algorithm to determine the optimal task sequencing strategy. This stage anticipates human behavior by proposing disassembly sequences aligned with expected human availability. At the second level, an online reactive adjustment refines the plan in real time, adapting it to actual human interventions and compensating for deviations from initial forecasts. The effectiveness of this global optimization strategy is evaluated against a non-global approach, in which the problem is partitioned into independent subproblems solved separately and then integrated. The results demonstrate the efficacy of the proposed approach in comparison with a non-global approach, particularly in scenarios where humans arrive earlier than anticipated. Full article