Search Results (564)

We engineered a compact methanol steam reforming (MSR) system tailored to power a 1 kW High-Temperature Proton Exchange Membrane (HT-PEM) fuel cell. The unit integrates an evaporator, reformer, and burner within a cylindrical titanium-alloy vacuum flask to minimize parasitic heat loss. Guided by an Artificial Intelligence Complex System Response (AICSR) framework, we developed a segmented catalyst architecture that positions an optimized Pd/ZnO/Al₂O₃ catalyst downstream of a commercial Cu–Zn catalyst bed. This spatial configuration reduces palladium consumption by >50% while maintaining a hydrogen generation rate of 8000 sccm at 250 °C. During a 40 h stability test, the system exhibited a low deactivation rate of 0.235% h⁻¹, with methanol conversion decaying gradually from 98.1% to 88.7%. The downstream PdZn intermetallic phase actively promoted the water–gas shift (WGS) reaction, restricting CO concentration to an average of 3.9% (minimum 2.5%). Achieving a system thermal efficiency of 88.589% and a 20 min startup time, this design validates AI-assisted spatial catalyst distribution as a highly viable strategy for compact hydrogen generation. Full article

The proton exchange membrane (PEM) electrolyzer is vital for converting surplus renewable energy (RE) into hydrogen, underpinning the efficient and stable operation of the electric–hydrogen system. However, frequent start–stop cycles and load variations accelerate the degradation of proton exchange membranes and catalyst layers, incurring significant lifetime costs that existing studies ignore. To explore how the PEM electrolyzer’s dynamic traits impact system performance, we introduce an optimized operation approach for the electricity–hydrogen integrated energy system (IES) that incorporates these dynamic features and the novel Loss of Life Cost (LLC) model. Initially, to rectify the inadequacy in modeling the PEM electrolyzer within the current electricity–hydrogen IES operational framework, we integrate its dynamic characteristics based on electrochemical properties and establish a quantitative relationship between operational cycles and degradation costs. This enhanced model accurately reflects how operational conditions affect the electrolyzer’s hydrogen production efficiency and lifetime consumption, enabling precise performance simulation and economic assessment. This, in turn, promotes high-quality renewable energy utilization via hydrogen production while ensuring asset longevity, meeting the rising demand for hydrogen energy applications. Building on this, we further factor in constraints related to diverse energy conversion and safe operation within the electricity–hydrogen IES, as well as the operational limits of hydrogen fuel cells, various energy storage (ES) options, cogeneration units, and other pertinent equipment, aiming to minimize the system’s total daily costs (operational plus degradation costs). Consequently, we develop an optimization operation model for the electricity–hydrogen IES that accounts for the PEM electrolyzer’s dynamic characteristics and degradation economics. Finally, through simulation examples validated against published experimental data, we comprehensively analyze how the PEM electrolyzer’s dynamic traits influence system operation, confirming the effectiveness of our proposed model and methodology. Simulation findings reveal that, under varying electrolyzer capacities, ignoring the PEM electrolyzer’s dynamic characteristics can result in a deviation in system operating. Compared with the proposed method, it can reduce the equipment degradation speed by a maximum of 5.78 times. Full article

The transportation sector’s decarbonization represents one of the most critical challenges in achieving global climate targets. This study presents a comprehensive analysis of an integrated renewable energy system that produces green hydrogen through a hybrid solar photovoltaic (PV) and wind power configuration. The proposed system combines a 1.2 MWp solar array with 800 kW wind turbines, feeding a 1 MW proton exchange membrane (PEM) electrolyzer for hydrogen production. The hydrogen is subsequently compressed, stored at 350 (for trucks and buses) and 700 bar (for cars), and then utilized either directly for fuel cell electric vehicles (FCEVs) or reconverted to electricity via a 250 kW stationary PEM fuel cell to support electric vehicle (EV) charging infrastructure. Through detailed techno-economic simulation using HOMER Pro and MATLAB/Simulink 2022a, we demonstrate that the hybrid configuration achieves a 71% electrolyzer capacity factor, producing 55.8 tonnes of hydrogen annually with a levelized cost of 5.82 €/kg. The system ensures over 60 h of grid-independent operation while reducing CO₂ emissions by 1656 tones annually compared to conventional grid-powered alternatives. Results indicate that hybrid renewable hydrogen systems can provide economically viable solutions for sustainable mobility infrastructure, with projected cost reductions making them competitive with fossil fuel alternatives by 2030. Full article

Proton Exchange Membrane (PEM) fuel cells represent a promising clean energy technology due to their high efficiency, environmental sustainability, and wide applicability in transportation and stationary power systems. Accurate parameter extraction from PEM fuel cell models is critical for reliable performance prediction, control, and optimization. However, this task is challenging because of the nonlinear, multimodal, and highly coupled characteristics of fuel cell models. To address this challenge, this paper proposes an Enhanced Elk Herd Optimizer (EEHO), incorporating a novel best-bull–guided differential reproduction mechanism to improve search accuracy, convergence speed, and robustness. The proposed enhancement enables a portion of offspring solutions to be generated by perturbing the global best solution using scaled differences between randomly selected herd members. This mechanism strengthens exploitation around promising regions while maintaining population diversity and preventing premature convergence. The EEHO is applied to extract seven unknown parameters of PEM fuel cell models by minimizing the sum of squared errors between experimental and simulated voltage data. The effectiveness of the proposed method is validated using two commercial PEM fuel cell stacks, namely a 250 W stack and a BCS 500 W stack. Extensive comparative evaluations against the conventional Elk Herd Optimizer and several well-established methods demonstrate that the EEHO achieves superior performance in terms of accuracy, convergence speed, robustness, and statistical consistency. The proposed algorithm attains lower error values, faster convergence, and more stable performance across multiple independent runs. Furthermore, the extracted parameters produce highly accurate voltage and power characteristics, closely matching experimental observations. The results confirm that the proposed EEHO provides an efficient, reliable, and robust optimization framework for PEM fuel cell parameter extraction and offers strong potential for broader applications in energy system modeling, intelligent optimization, and renewable energy optimization problems. Quantitatively, the proposed EEHO achieved a significant reduction in the averages of the Sum of Squared Errors (SSE) of up to 24.96% and 23.29% compared with the conventional EHO for the 250 W stack and a BCS 500 W stack, respectively, demonstrating its superior accuracy in parameter estimation. To further validate the robustness and generalization capability of the proposed EEHO, two additional commercial PEM fuel cell datasets, of Ballard Mark V and Modular SR-12, are investigated and compared against several state-of-the-art optimization algorithms. The results, supported by Wilcoxon and Friedman statistical tests and boxplot analyses, confirm that EEHO consistently achieves superior accuracy, stability, and convergence reliability across different operating conditions. Full article

In this work the feasibility of fully autonomous hydrogen homes designed for complete off-grid operation is presented. A detailed mathematical modeling and optimization model is developed to evaluate the technical performance and economic feasibility of hydrogen fuel cell-powered residential systems with no grid connection or fallback. The system integrates primary and standby Proton Exchange Membrane (PEM) fuel cells, multi-day hydrogen storage, advanced power conditioning, and comprehensive controls to achieve reliable year-round power supply. The analysis encompasses a complete 20-year lifecycle cost assessment. The results demonstrate that fully autonomous hydrogen homes achieve 99.85% system availability with 13.1 h of potential downtime annually, providing reliable energy independence. The levelized cost of electricity over the 20-year system lifetime is calculated at 0.4543 US$/kWh at baseline hydrogen prices of 6 US$/${\mathrm{kg}}_{{\mathrm{H}}_2}$, substantially higher than grid-connected alternatives. The analysis identifies critical sensitivity to hydrogen pricing and demonstrates that at hydrogen costs below 3 US$/${\mathrm{kg}}_{{\mathrm{H}}_2}$ (achievable with mature green hydrogen production), competitive payback periods of 12–15 years are possible in high-cost electricity regions. This study concludes that hydrogen-based autonomous homes represent a viable long-term solution for residential energy independence, particularly in remote or off-grid locations where grid connection is impractical or in regions with high electricity tariffs and developing green hydrogen production capacity. Full article

Proton exchange membrane fuel cells (PEMFCs) are considered to be a promising solution for clean energy conversion in hydrogen electric vehicles. Accurate voltage prediction is crucial for designing efficient energy management and control strategies. While deep neural networks have shown good potential in modeling PEMFCs, the role of optimization algorithms and training performance criteria in achieving accurate voltage predictions remains unclear. This research aims to carry out a comprehensive comparative study using three popular optimization algorithms and different performance criteria including prediction accuracy, convergence speed, and training stability. A real experimental dataset for a Nexa PEMFC system has been used to train and evaluate different models of artificial neural networks (ANNs) to find out which optimization algorithm and performance criteria are best for efficient modeling of PEMFCs under varying operating conditions. The results of this study are analyzed through a comparative evaluation of different metaheuristic optimization algorithms applied within a unified ANN training framework for PEMFC voltage prediction. Particle swarm optimization (PSO) provides the highest voltage prediction accuracy and robust convergence behavior, whereas Grey Wolf Optimization (GWO) achieves the fastest convergence with reduced computational time. Full article

This paper reviews artificial intelligence (AI) applications in the design and optimization of proton exchange membrane (PEM) materials for hydrogen fuel cells. Clean energy conversion is a substantial benefit of PEM fuel cells, which conventional membrane development struggles with due to time-consuming trial-and-error methods, which are not adequate in capturing the different interdependencies of the membrane structure, and environmental variables. The review establishes foundational design principles of PEMs and outlines their challenges and computational methodologies are constructed to address them. Various advanced AI methods have been highlighted which include graph neural networks, multitask frameworks, and physics-informed models that facilitate rapid prediction of polymer properties. Optimization methods have been reported with 10–30% performance improvements, for instance, NSGA-II frameworks achieving 13–27% gains in power density. Experimental requirements are reduced by 40–60%, as seen with Bayesian optimization, identifying optimal designs within as few as 40 iterations. Current challenges include data availability, generalizability, and scalability, which are closely assessed in this review. Full article

The decarbonization of large cruise ships is challenged by their extreme and tightly coupled electrical, thermal, and cooling demands. This study investigates a liquid hydrogen (LH₂)-based tri-generation system for cruise ships that simultaneously supplies electricity, heat, and cooling. Key novelties include the use of LH₂ as the onboard energy carrier for large cruise ships, the recovery of cooling energy from LH₂, a dynamic control strategy that synergistically modulates PEM fuel cell utilization to regulate downstream catalytic burner heat generation and balance heat and electricity generation and demand, and the first full-scale cruise-ship model of such a system, including hydrogen consumption and onboard storage sizing. A dynamic system-level model is applied to a representative 7-day voyage of a large cruise ship. The results show that the proposed system can meet combined peak demands of approximately 61 MW while achieving overall system efficiencies approaching 75%. Compared to traditional marine diesel-based power plants, the LH₂-based tri-generation configuration improves system efficiency by more than 20 percentage points. Total hydrogen consumption is estimated at approximately 240 t, which can be reduced by about 20% through shore-to-ship power, yielding a system volume comparable to that of a conventional diesel-based power plant. These results demonstrate the technical feasibility and system-level advantages of LH₂-based tri-generation for zero-emission cruise ships. Full article

Proton exchange membrane fuel cells (PEMFCs) experience significant performance degradation over long-term operation, hindering their commercial viability. Accurately identifying polarization curve parameters during aging is crucial for elucidating degradation mechanisms and enabling health monitoring, yet this task faces challenges such as parametric coupling and pronounced nonlinearity. This study tackles these identification challenges through the integrated application of a dynamic aging model, which captures the synergy between degradation mechanisms like platinum oxidation and membrane resistance increase, and the introduction of the novel Threat Response Adaptive Differential Evolution (TRADE) algorithm. The algorithm employs multidimensional threat assessment, a three-tier response strategy, and adaptive decision-making to achieve accurate and robust parameter identification. Validated with experimental data from commercial PEMFC stacks over a full ageing cycle, the TRADE algorithm achieves a root mean square error as low as 0.00675 V within the 100–1000 h range, demonstrating superior fitting performance and stability. Sensitivity analysis further reveals that activation overpotential is the dominant degradation mechanism throughout the entire cycle (contributing ≥ 70%), whereas the contribution of concentration overpotential rises substantially to 33% under high-current-density conditions. This study provides a robust modelling framework and an effective methodology for quantifying PEMFC ageing mechanisms, predicting remaining useful life, and optimizing system performance. Full article

Proton exchange membrane fuel cells (PEMFCs) are attractive energy sources for clean and efficient power generation; however, their nonlinear characteristics and sensitivity to operating condition variations make maximum power point tracking (MPPT) a challenging control problem. Conventional MPPT techniques often exhibit slow convergence, steady-state oscillations, and degraded performance under dynamic fuel flow variations. This paper proposes a machine learning–driven MPPT control strategy for a PEMFC system integrated with a DC–DC boost converter. The MPPT problem is formulated as a supervised classification task, where machine learning classifiers generate duty-cycle commands to regulate the converter and ensure operation at the maximum power point. A detailed PEMFC–converter model is developed in MATLAB/Simulink-2025b, and a dataset of 3000 labeled samples is generated under varying fuel flow conditions. Several classification algorithms, including decision trees, support vector machines (SVM), k-nearest neighbors (kNN), and ensemble learning methods, are systematically evaluated within an identical simulation framework. Simulation results show that the proposed machine learning-based MPPT controller significantly improves dynamic and steady-state performance. Ensemble Boosted Trees achieve the best overall response with a settling time of approximately 32 ms, peak power overshoot below 4.5%, and steady-state power ripple limited to 1.5%. Quadratic SVM and weighted kNN classifiers also demonstrate stable tracking behavior with power ripple below 2.1%, while overly complex models such as Cubic SVM suffer from large oscillations and reduced accuracy. These results confirm that classification-based machine learning offers an effective, fast, and robust MPPT solution for PEMFC systems under dynamic operating conditions. Full article

Proton exchange membranes (PEMs) are essential for fuel cells, yet conventional materials like Nafion suffer from humidity dependence and limited thermal stability. This study introduces sulfonated phenylene-bridged periodic mesoporous organosilicas (PMOs) as promising inorganic–organic hybrid PEMs, synthesized via surfactant-templating with varying alkyl chain lengths for different mesopore sizes. Post-synthetic functionalization involves nitration of phenylene moieties, reduction to amines, and ring-opening of propane or butane sultones to graft sulfonic acid groups via flexible spacers, achieving homogeneous distribution along pore walls. Post-functionalization is confirmed by powder X-ray diffraction (PXRD), revealing preserved 2D hexagonal p6mm ordering and phenylene stacking. N₂ physisorption shows type IV isotherms with reduced pore volumes and pore sizes. ¹H NMR is used to quantify functionalization degrees. Impedance spectroscopy on pressed pellets demonstrates proton conductivities up to 2 × 10⁻³ S cm⁻¹ at 30 °C and 90% RH, depending on the functionalization degree, confirming sulfonic acid-mediated conduction. 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

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

To accurately investigate the loss characteristics of fuel cell vehicles (FCVs) under actual operating conditions and enhance their power performance and economic efficiency, this study establishes a numerical model of the proton exchange membrane fuel cell (PEMFC) stack based on real-world data from the second-generation Mirai. The stack model incorporates leakage current losses and imposes a limit on maximum current density. Besides, this study analyzes the effects of operating parameters (PEM water content, hydrogen partial pressure, current density, oxygen partial pressure, and operating temperature) on stack power output, efficiency, and eco-performance coefficient (ECOP). Furthermore, Non-Dominated Sequential Genetic Algorithm (NSGA-II) is employed to optimize the PEMFC stack performance, yielding the optimal operating parameter set for FCV operation. Further simulations are conducted on dynamic performance characteristics of the second-generation Mirai under two typical driving cycles, evaluating the power performance and economy of the FCV before and after optimization. Results demonstrate that the established PEMFC stack model accurately analyzes the output performance of an actual FCV when compared with real-world performance test data from the second-generation Mirai. Through optimization, output power increases by 7.4%, efficiency improves by 1.95%, and ECOP rises by 3.84%, providing guidance for enhancing vehicle power performance and improving overall vehicle economy. This study provides a practical framework for enhancing the power performance and overall energy sustainability of fuel cell vehicles, contributing to the advancement of sustainable transportation. Full article