Search Results (879)

This study presents a first-of-its-kind investigation into retrofitting domestic vessels with a novel hybrid system integrating a Solid Oxide Fuel Cell (SOFC) and an Internal Combustion Engine (ICE). Using a Lake Ferry and an Island Ferry as case studies, three power-sharing scenarios (10–20% SOFC contribution) were examined for cruise and port operations. The results show that increasing the SOFC power share enhances overall system efficiency, reducing daily fuel energy consumption by up to 9% while achieving SOFC efficiencies of 58–60% in port. The design analysis confirms the physical retrofit feasibility for both vessels, with all scenarios occupying 72–92% of available machinery space. However, increasing the SOFC share from 10% to 15–20% raised total system weight by 10–20% and volume by 12–27%. Economically, the system demonstrates strong viability for high-utilization vessels, with Levelized Cost of Energy (LCOE) values of 236–248 EUR/MWh, while the sensitivity analysis highlights the SOFC capital cost as the dominant economic driver. Environmentally, the hybrid system achieves annual CO₂ reductions of 46–51% and NOx reductions of 51–62% compared to conventional diesel systems, with zero NOx emissions in port. The SOFC-ICE hybrid system proves to be a robust transitional pathway for maritime decarbonization, particularly for vessels with significant port-side operating hours. Full article

High-temperature proton exchange membranes (HT-PEMs) are critical components of high-temperature fuel cells, facilitating proton transport and acting as a barrier to fuel and electrons; however, their performance is hampered by persistent issues of phosphoric acid leaching and oxidative degradation. Herein, a novel HT-PEM with abundant hydrogen bond network is constructed by incorporating nanoscale polyhedral oligomeric silsequioxane functionalized with eight pendent sulfhydryl groups (POSS-SH) into poly(4,4′-diphenylether-5,5′-bibenzimidazole) (OPBI) matrix. POSS, a cage-like nanostructured hybrid molecule, features a well-defined silica core and highly designable surface organic groups, offering unique potential for enhancing membrane performance at the molecular level. Through controlled reactions between sulfhydryl groups and allyl glycidyl ether (AGE), two functional POSS crosslinkers—octa-epoxide POSS (OE-POSS) and mixed sulfhydryl-epoxy POSS (POSS-S-E)—were synthesized. These were subsequently used to fabricate crosslinked OPBI membranes (OPBI-OE-POSS and OPBI-POSS-S-E) via epoxy–amine coupling. The OPBI-POSS-S-E membranes demonstrated exceptional oxidative stability, which is attributed to the free radical scavenging ability of the retained sulfhydryl groups on the nano-sized POSS framework. After soaking in Fenton’s reagent at 80 °C for 108 h, the OPBI-POSS-S-E-20% membrane retained 79.4% of its initial weight, significantly surpassing both the OPBI-OE-POSS-20% and pristine OPBI membranes. The PA-doped OPBI-POSS-S-E-20% membrane achieved a proton conductivity of 50.8 mS cm⁻¹ at 160 °C, and the corresponding membrane electrode assembly delivered a peak power density of 724 mW cm⁻², highlighting the key role of POSS as a nano-modifier in advancing HT-PEM performance. Full article

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

Decarbonisation of the aviation sector is essential for achieving global-climate targets, with hydrogen propulsion emerging as a viable alternative to battery–electric systems for vertical flight. Unlike previous studies focusing on clean-sheet eVTOL concepts or fixed-wing platforms, this work provides a comprehensive retrofit evaluation of a two-seat light helicopter (Cabri G2/Robinson R22 class) to a hydrogen–electric hybrid powertrain built around a Toyota TFCM2-B PEM fuel cell (85 kW net), a 30 kg lithium-ion buffer battery, and 700 bar Type-IV hydrogen storage totalling 5 kg, aligned with the Vertical Flight Society (VFS) mission profile. The mass breakdown, mission energy equations, and segment-wise hydrogen use for a 100 km sortie are documented using a single main rotor with a radius of R = 3.39 m, with power-by-segment calculations taken from the team’s final proposal. Screening-level simulations are used solely for architectural assessment; no experimental validation is performed. Mission analysis indicates a 100 km operational range with only 3.06 kg of hydrogen consumption (39% fuel reserve). The main contribution is a quantified demonstration of a practical retrofit pathway for light rotorcraft, showing approximately 1.8–2.2 times greater range (100 km vs. 45–55 km battery-only baseline, including respective safety reserves). The Hawk demonstrates a 28% reduction in total propulsion system mass (199 kg including PEMFC stack and balance-of-plant 109 kg, H₂ storage 20 kg, battery 30 kg, and motor with gearbox 40 kg) compared to a battery-only configuration (254.5 kg battery pack, plus equivalent 40 kg motor and gearbox), representing approximately 32% system-level mass savings when thermal-management subsystems (15 kg) are included for both configurations. Full article

Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance ($f$) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance $f$ and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R² increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems. Full article

Energy management strategies (EMSs) play a critical role in improving both the efficiency and durability of fuel cell electric vehicles (FCEVs). To overcome the limited adaptability and insufficient durability consideration of existing deep reinforcement learning-based EMSs, this study develops a degradation-aware energy management strategy based on the Soft Actor–Critic (SAC) algorithm. By leveraging SAC’s maximum-entropy framework, the proposed method enhances exploration efficiency and avoids premature convergence to operating patterns that are unfavorable to fuel cell durability. A reward function explicitly penalizing hydrogen consumption, power fluctuation, and degradation-related operating behaviors is designed, and the influences of reward weighting and key hyperparameters on learning stability and performance are systematically analyzed. The proposed SAC-based EMS is evaluated against Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) strategies under both training and unseen driving cycles. Simulation results demonstrate that SAC achieves a superior and robust trade-off between hydrogen economy and degradation mitigation, maintaining improved adaptability and durability under varying operating conditions. These findings indicate that integrating degradation awareness with entropy-regularized reinforcement learning provides an effective framework for practical EMS design in FCEVs. Full article

Unmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks. Full article

Developing cost-effective and durable electrocatalysts is essential for advancing anion exchange membrane fuel cells (AEMFCs). This work evaluates Pd-based catalysts supported on β-MnO₂, Vulcan carbon (C), and their physical blend (Pd/MnO₂:Pd/C) as bifunctional electrodes for the oxygen reduction reaction (ORR) and hydrogen oxidation reaction (HOR). The catalysts were synthesized via chemical reduction and characterized by TGA, ICP-OES, TEM, BET, and XRD. Rotating disk electrode studies revealed that the hybrid exhibited superior activity and kinetics, with lower Tafel slopes and higher exchange current densities compared to the individual supports. In AEMFCs, the hybrid reached 128.0 mW cm⁻² as a cathode and 221.7 mW cm⁻² as an anode, outperforming individual components. This enhanced performance arises from the synergistic interaction between Pd nanoparticles and MnO₂, where MnO₂ modulates the catalyst’s microstructure and local reaction environment while the carbon phase ensures efficient electron transport. MnO₂, although inactive for the HOR alone, acted as a structural spacer, enhancing mass transport and stability. Durability tests confirmed that the hybrid electrocatalyst retained over 99% of its initial activity after 3000 cycles. These results highlight the hybrid Pd/MnO₂:Pd/C as a promising, bifunctional, and durable electrocatalyst for AEMFC applications. Full article

Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

Fuel cell commercial vehicles are widely used in commercial transport for their high efficiency and long range. However, in mixed operating scenarios, their energy economy and fuel cell operational stability cannot be fully balanced. Traditional strategies lack adaptability in mixed operating scenarios. Therefore, based on the equivalent factor regulation formula of the Adaptive Equivalent Hydrogen Consumption Minimization Strategy (A-ECMS) and the improved Sparrow Search Algorithm-Long Short-Term Memory (SSA-LSTM) hybrid model, short-term speed prediction and three-stage speed interval division are embedded into the equivalent factor regulation logic. A dynamic equivalent factor regulation strategy integrating SOC deviation is constructed, and an improved Predictive Equivalent Hydrogen Consumption Minimization Strategy (P-ECMS) is finally derived. The SSA-LSTM algorithm is optimized via constrained hyperparameter tuning for short-term speed prediction. A time-decay weighting mechanism enhances recent speed data weight, with weighted results as inputs to boost accuracy. Moving Average Residual Correction (MARC) is used to verify the speed prediction model accuracy and correct residuals. Multi-scenario tests show that the SSA-LSTM model outperforms the Gated Recurrent Unit (GRU) model in prediction accuracy and generalization ability, providing reliable data support for segmented regulation. With battery SOC deviation and the SSA-LSTM-predicted speed trend as core inputs, combined with three-stage speed interval division, A-ECMS’s equivalent factor regulation formula is improved. The model adopts a segmented dynamic regulation logic to integrate dual factors into equivalent factor adjustment, and it reasonably adjusts the energy output ratio of fuel cells and power batteries according to speed intervals and operating condition changes. In scenarios with significant speed fluctuations and frequent operating condition transitions, power shocks are mitigated by the power battery’s peak-shaving and valley-filling function. Simulation results for C-WTVC and NREL2VAIL show that, compared with traditional A-ECMS, the improved P-ECMS has notable energy benefits, with equivalent hydrogen consumption reduced by 3.41% and 5.48%, respectively. The fuel cell’s state is significantly improved, with its high-efficiency share reaching 63%. The output power curve is smoother, start–stop losses are reduced, and the fuel cell’s service life is extended, balancing the energy economy and component durability. Full article

The increasing impact of global warming is predominantly driven by the extensive use of fossil fuels, which release significant amounts of greenhouse gases into the atmosphere. This has led to a critical need for alternative, sustainable energy sources that can mitigate environmental impacts. Photovoltaic technology has emerged as a promising solution by harnessing renewable energy from the sun, providing a clean and inexhaustible power source. Perovskite solar cells (PSCs) are a class of hybrid organic–inorganic solar cells that have recently attracted significant scientific attention due to their low cost, relatively high efficiency, low–temperature processing routes, and longer carrier lifetimes. These characteristics make them a viable alternative to traditional fossil fuels, reducing the carbon footprint and contributing to the fight against global warming. In this study, the SCAPS–1D numerical simulator was used in the computational analysis of a PSC device with the configuration FTO/ETL/BaZr(S_0.6Se_0.4)₃/HTL/Ir. Different hole transport layer (HTL) and electron transport layer (ETL) material were proposed and tested. The HTL materials included copper (I) oxide (Cu₂O), 2,2′,7,7′–Tetrakis(N,N–di–p–methoxyphenylamine)9,9′–spirobifluorene (spiro–OMETAD), and poly(3–hexylthiophene) (P₃HT), while the ETLs included cadmium suphide (CdS), zinc oxide (ZnO), and [6,6]–phenyl–C₆₁–butyric acid methyl ester (PCBM). Finally, BaZr(S_0.6Se_0.4)₃ was proposed as an absorber, and a fluorine–doped tin oxide glass substrate (FTO) was proposed as an anode. The metal back contact used was iridium. Photovoltaic parameters such as short circuit density (I_sc), open circuit voltage (V_oc), fill factor (FF), and power conversion efficiency (PCE) were used to evaluate the performance of the device. The initial simulated primary device with the configuration FTO/CdS/BaZr(S_0.6Se_0.4)₃/spiro–OMETAD/Ir gave a PCE of 5.75%. Upon testing different HTL materials, the best HTL was found to be Cu₂O, and the PCE improved to 9.91%. Thereafter, different ETLs were also inserted and tested, and the best ETL was established to be ZnO, with a PCE of 10.10%. Ultimately an optimized device with a configuration of FTO/ZnO/BaZr(S_0.6Se_0.4)₃/Cu₂O/Ir was achieved. The other photovoltaic parameters for the optimized device were as follows: FF = 31.93%, J_sc = 14.51 mA cm⁻², and V_oc = 2.18 V. The results of this study will promote the use of environmentally benign BaZr(S_0.6Se_0.4)₃–based absorber materials in PSCs for improved performance and commercialization. Full article

This review examines modern approaches to layer formation in solid oxide fuel cells (SOFCs), focusing on traditional, thin-film, and additive manufacturing methods. A systematic comparison of technologies, including slip casting, screen printing, CVD, PLD, ALD, HiPIMS, inkjet, aerosol, and microextrusion printing, is provided. It is shown that traditional methods remain technologically robust but are limited in their capabilities for miniaturization and interfacial architecture design. Modern thin-film and additive approaches provide high spatial accuracy, improved ion-electron characteristics, and flexibility in the design of multilayer structures; however, they require addressing issues related to scalability, ink stability, interfacial compatibility, and reproducibility. Particular attention is paid to interfacial engineering methods, such as functionally graded layers, nanostructured infiltration, and temperature-controlled 3D printing. Key challenges are discussed, including thermal instability of materials, the limited gas impermeability of ultra-thin electrolytes, and degradation during long-term operation. Development prospects lie in the integration of hybrid methods, the digitalization of deposition processes, and the implementation of intelligent control of printing parameters. The presented analysis forms the basis for further research into the scalable and highly efficient production of next-generation SOFCs designed for low-temperature operation and long-term operation in future energy systems. Full article

This study presents the design, real-time implementation, and full-scale experimental validation of a rule-based Energy Management Strategy (EMS) for a dual-stack Fuel Cell Hybrid Electric Vehicle (FCHEV) developed on a Jeep Wrangler platform. Unlike previous studies, predominantly focused on simulation-based analysis or single-stack architectures, this work provides comprehensive vehicle-level experimental validation of a deterministic real-time EMS applied to a dual fuel cell system in an SUV-class vehicle. The control algorithm, deployed on a National Instruments CompactRIO embedded controller, ensures deterministic real-time energy distribution and stable hybrid operation under dynamic load conditions. Simulation analysis conducted over eight consecutive WLTC cycles shows that both fuel cell stacks operate predominantly within their optimal efficiency range (25–35 kW), achieving an average DC efficiency of 68% and a hydrogen consumption of 1.35 kg/100 km under idealized conditions. Experimental validation on the Wrangler FCHEV demonstrator yields a hydrogen consumption of 1.67 kg/100 km, corresponding to 1.03 kg/100 km·m² after aerodynamic normalization (Cd·A = 1.624 m²), reflecting real-world operating constraints. The proposed EMS promotes fuel-cell durability by reducing current cycling amplitude and maintaining operation within high-efficiency regions for the majority of the driving cycle. By combining deterministic real-time embedded control with vehicle-level experimental validation, this work strengthens the link between EMS design and practical deployment and provides a scalable reference framework for future hydrogen powertrain control systems. Full article