Search Results (1,746)

This paper presents a model predictive control (MPC)-based energy management strategy for hybrid power systems combining a proton-exchange membrane fuel cell (PEMFC) with a lithium iron phosphate (LFP) battery storage unit for renewable energy applications. The proposed framework optimizes power allocation between the two sources while respecting operational constraints, including current limits, power balance requirements, and state-of-charge (SOC) bounds with soft constraints to prevent overcharging and deep discharging. Unlike conventional rule-based approaches, the MPC formulation employs a quadratic cost function with tunable weighting factors that enable flexible prioritization of either fuel cell conservation or battery lifetime extension. Accurate yet computationally efficient models are developed for both components: an equivalent circuit model for the LFP battery and a theoretical electrochemical model for the PEMFC. The performance of the proposed strategy is validated through comprehensive simulations under realistic renewable generation and load profiles. Five case studies are examined, each representing different operational scenarios characterized by varying initial SOC conditions and component prioritization weights. The results demonstrate that the MPC-based approach effectively manages power distribution, maintains SOC within safe operating ranges, and adapts to changing system conditions. Quantitative analysis shows that the tunable weighting strategy successfully limits high-current events, reducing high-current operation and potentially mitigating current-related degradations. The proposed framework offers a scalable and flexible solution for improving the reliability of hybrid energy storage in modern renewable grids. Full article

Green hydrogen production through water electrolysis is a key pathway to the decarbonization of future energy systems. However, part of the electrical input is transformed into waste heat. In this study, alkaline, proton-exchange membrane, anion-exchange membrane, and solid oxide electrolysis systems are compared in terms of operating temperature, heat generation, heat transfer medium, and integration constraints. Reported COP values for commercial high-temperature vapour-compression heat pumps range from 2.4 to 5.8, depending on operating conditions. The heat-pump technologies reviewed include vapour-compression systems with single-stage, multistage, cascade, and transcritical configurations, together with absorption and adsorption systems, with a focus on suitable working fluids and practical limitations. The review distinguishes between direct heat recovery and heat recovery assisted by heat pumps, and it identifies two main areas of application: external supply for district heating, industrial consumers, and energy communities; and internal support for feedwater preheating, water cycle integration, and steam generation. A selection framework is proposed in which source- and sink-temperature compatibility determines thermodynamic feasibility, COP characterizes heat-pump performance, and LCoH supports techno-economic comparison. Direct heat recovery should be preferred when temperatures are compatible, while heat pumps can operate as enabling technologies when temperature upgrading is required and system-level economic and environmental performance remains advantageous. Full article

The conversion of hydrogen into electricity is a subject of intense interest. Various technologies are available at laboratory level, but in industrial applications, the most established of them is the proton exchange membrane-based fuel cell. This increased interest, combined with the maturity of the technology, has led to attempts to integrate fuel cells into hybrid systems with renewable energy sources. Such hybrid systems are subject to numerous studies, most of which are aimed at optimizing installed capacity in order to reduce the cost of energy production, of investment, etc. This article aims to provide an experiment-based approach for a simple yet accurate model linking electrical power with hydrogen consumption that can be used in the design and optimization procedures for hybrid systems. For this purpose, an experimental setup with a 1 kW fuel cell was built at the Technical University of Sofia, where experimental studies were conducted to enable the development of the model. Although the proposed model does not reflect the influence of certain technological factors on the operation of the fuel cell, it provides sufficient accuracy to determine the hydrogen consumption for a given electrical power when used in operation simulations of the hybrid system. Full article

Renewable-driven operation exposes proton-exchange membrane (PEM) electrolyzers to ramps, starts, and partial-load conditions that accelerate degradation and weaken scheduling reliability. This paper develops a health-aware scheduling framework for an integrated electro-hydrogen system. It represents operating stress, delayed response, irreversible degradation, recoverable performance loss, and efficiency feedback. Scheduled rest partially relaxes the recoverable state, while per-unit health budgets yield degradation shadow prices that redirect load from health-scarce stacks. A Wasserstein distributionally robust model implements a health-conditioned risk-aversion policy by adjusting the protection radius with pre-horizon fleet health and filtered stress. The tractable finite-support formulation is evaluated through progressive ablations and five uncertainty treatments using chronological Liaoning wind, solar, and load data. Rotational recovery provides the main degradation mitigation, whereas shadow prices primarily improve allocation among heterogeneous stacks. Compared with fixed-radius DRO, the health-conditioned policy reduced the point estimates of ${\mathrm{CVaR}}_{95}$, energy-violation rate, and severe health-budget exceedance by 3.91%, 1.68 percentage points, and 3.36 percentage points, respectively. These results indicate the value of coordinating equipment health and uncertainty protection in short-term electro-hydrogen scheduling. Full article

In the current situation of carbon reduction in the shipping industry, hydrogen as a future energy source is the key to hydrogen-powered ships. Onboard methanol steam reforming (MSR) hydrogen production technology is of great significance in solving the hydrogen safety issues of hydrogen-powered ships. The key to this technology is to obtain MSR catalysts with lower CO selectivity and higher hydrogen production performance for use as hydrogen sources in high-temperature proton-exchange membrane fuel cells (HT-PEMFCs). A new Cu_0.75Mg_0.25Ga_0.7Fe_1.3O₄ spinel catalyst was synthesized and subsequently dispersed at a nominal loading of 15 wt% onto three distinct oxide supports: CeO₂, ZrO₂, and Cr₂O₃. The synthesized samples were characterized, and their catalytic behavior in MSR was evaluated in a fixed-bed reactor. The results establish that the loading procedure leaves the bulk spinel structure intact. Among the series, the CeO₂-supported specimen exhibits a higher degree of active-phase dispersion, a loosely packed and highly porous architecture, and more extensive interfacial contact between the spinel crystallites and the support. Relative to its ZrO₂- and Cr₂O₃-supported counterparts, Cu_0.75Mg_0.25Ga_0.7Fe_1.3O₄@CeO₂ delivers markedly superior overall catalytic performance. Under conditions of 320 °C, a water-to-methanol molar ratio of 3:1, and a liquid hourly space velocity (LHSV) of 15 h⁻¹, complete methanol conversion (100%) is achieved, together with a hydrogen production rate of 8.71 mmol·min⁻¹·gcat⁻¹ and CO selectivity approaching zero. The outstanding catalytic behavior may be attributed to the interface synergy between the spinel active center and CeO₂ support, particularly the synergistic effect of oxygen vacancies and Ce⁴⁺/Ce³⁺ redox shuttle, which facilitates substrate replacement while inhibiting CO production. This catalyst architecture provides a promising material platform for on-line methanol reforming integrated with fuel cell power systems on hydrogen-powered ships. Full article

Dynamic inductive charging (DIC) combined with hybrid energy storage systems (HESSs) and vehicle-to-grid (V2G) capabilities offers a promising pathway toward extended-range electric vehicles with grid integration benefits. However, real-time optimal energy management remains challenging due to multi-axis coil misalignment, component aging, and bidirectional power flow uncertainty. This paper proposes an adaptive digital twin driven artificial intelligence (AI) energy management framework integrating physics-informed neural networks (PINNs), soft actor critic (SAC) deep reinforcement learning, and model predictive control (MPC) for optimal power distribution among a proton exchange membrane fuel cell (PEMFC), lithium-ion battery, supercapacitor, dynamic wireless charging, and grid interface in four-wheel drive electric vehicles (4WD-EVs). The framework features: (1) a self-evolving digital twin with online learning via Elastic Weight Consolidation (EWC) updating every 50 cycles; (2) a PINN-based state estimator for battery-state estimation, with an average inference time of 1.1 ms and a worst-case latency of 2.8 ms; (3) a hierarchical SAC–MPC strategy with high-level mode selection and low-level power optimization; (4) real-time five-degree-of-freedom WPT misalignment compensation, achieving a mean efficiency of 91.5% under the evaluated dynamic lateral misalignment conditions, with a 50 mm displacement amplitude; (5) degradation-aware V2G optimization generating €582.50/year in revenue while reducing battery aging by 31.8%; and (6) comprehensive techno-economic analysis yielding a discounted payback period of approximately 5.57 years and a net present value of approximately €3777 over a 10-year horizon. Validated through 200+ hours of hardware-in-the-loop (HIL) simulation on the dSPACE/NVIDIA Jetson platform, the proposed approach achieves a 24.3% cost reduction and 31.8% lower battery degradation. The MPC controller exhibits an average execution time of 32.1 ms, a 95th-percentile latency of 44.8 ms, and a worst-case latency of 62.4 ms, while remaining within the 100-ms real-time control deadline. Results demonstrate the viability of adaptive digital twins for next-generation EVs with autonomous charging and multi-source architectures. Full article

Redox flow batteries (RFBs) are an interesting option for long-term energy storage. A well-performing membrane sits at the heart of the electrochemical battery cell and should effectively mitigate crossover of active species while minimizing resistance. However, current commercial membranes are rather expensive and demonstrate sub-optimal performance, leading to an extensive search for alternatives. Research on membranes for RFBs has long been dominated by dense ion-exchange membranes and porous membranes, both potentially with fillers. In recent years, increased interest in alternative morphologies such as thin-film composites (TFCs) has ignited new research directions. TFCs consist of a thin dense layer on top of a porous support, aiming to merge the advantages of both. Traditionally, TFCs are made using polyamide top layers. In this paper, a novel chemistry is developed with increased chemical stability for RFBs. Poly(vinylbenzyl chloride) is crosslinked interfacially with a diamine, demonstrating for the first time the potential of support-mediated interfacial crosslinking with two immiscible solvents. Optimization of the support, amine crosslinker, reaction time and synthesis procedure allowed a shift of the trade-off between vanadium crossover and proton transport, highlighting the opportunities for this promising TFC chemistry. Full article

The design of efficient small-scale hydrogen energy storage systems requires balancing hydrogen production rate, electrical efficiency, and system simplicity. This study experimentally investigates the energetic compromise imposed by separator material and electrolyzer architecture through a comparative analysis of finite-gap alkaline, finite-gap acidic, and zero-gap proton exchange membrane (PEM) electrolyzers. Zirfon^® Pearl 500 (Agfa, Mortsel, Belgium) diaphragms were employed in alkaline electrolysis using 25 wt.% KOH, whereas Nafion™ 117 (Chemours, Wilmington, DE, USA) membranes were used in both finite-gap acidic electrolysis (2.55 M H₂SO₄) and a commercial five-cell zero-gap PEM electrolyzer supplied with deionized water. Electrochemical performance was evaluated in terms of polarization behavior, apparent resistance, hydrogen production rate, Faradaic efficiency, and energy conversion efficiency. The zero-gap PEM architecture exhibited the best electrochemical performance, with an apparent resistance of only 0.138 Ω per cell, corresponding to reductions of approximately 43-, 51-, and 64-fold compared with the finite-gap PEM, stainless steel/Zirfon alkaline, and nickel/Zirfon alkaline configurations, respectively. The zero-gap electrolyzer delivered currents from 1.53 to 10.0 A while operating below 2.8 V, demonstrating the benefit of minimizing the ionic transport path. In contrast, the finite-gap acidic configuration achieved higher hydrogen production rates than the alkaline system owing to the superior proton conductivity of Nafion™ 117, whereas the alkaline Ni/Zirfon configuration reached the highest Faradaic efficiency (≈98%) and energy conversion efficiency (≈36%) because of improved gas separation and reduced hydrogen crossover. Electrochemical impedance spectroscopy further revealed that the normalized ohmic resistance of the zero-gap PEM cell was only 0.029 Ω, with charge-transfer processes accounting for approximately 96.2% of the total impedance. These results demonstrate that separator properties and cell architecture govern the trade-off between reaction kinetics and energy efficiency, providing practical guidelines for selecting electrolyzer configurations dedicated to decentralized and small-scale hydrogen energy storage. Full article

The coupled effects of power distribution and stack temperature strongly influence hydrogen economy, durability, and operating stability in proton exchange membrane fuel cell (PEMFC) vehicles. This study proposes an integrated energy–thermal management strategy based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm for a PEMFC hybrid bus. The energy management agent regulates PEMFC power using vehicle demand, battery state of charge, and stack temperature, while the thermal management agent controls coolant and air mass flow rates using temperature errors and commanded PEMFC power. Under the unseen CHTC-C cycle, MADDPG reduces equivalent hydrogen consumption by 0.49% and 1.79% compared with SAC and DDPG, respectively, while remaining 2.92% above the offline dynamic programming benchmark. Under an independent real-world bus cycle, MADDPG reduces the maximum stack outlet temperature deviation by 98.56% and 98.07%relative to SAC and MPC, respectively. Additional tests under ambient temperature and aging variations show bounded thermal responses without retraining, and HIL experiments confirm real-time execution at a 1 s control period. Overall, the proposed strategy improves energy economy, thermal regulation, adaptability, and real-time applicability through coordinated power–temperature information exchange. Full article

A leaf-vein–serpentine composite flow field (L-SFF) is proposed to improve reactant distribution, water management, and electrochemical performance of proton exchange membrane fuel cells (PEMFCs). The L-SFF consists of a main trunk and multilevel branches inspired by coconut palm leaf veins, with a branching angle of 45° and a branch width of 2 mm. Three-dimensional multiphysics models of the traditional serpentine flow field (SPFF), cathode leaf-vein–serpentine flow field (S-LFF), and anode leaf–vein–serpentine flow field (L-SFF) were developed using COMSOL Multiphysics 6.1 to investigate current density, membrane water content, temperature distribution, and power output. Under 0.6 V, 353 K, and 1 bar conditions, the L-SFF achieves a maximum power density of 0.538 W·cm⁻², approximately 2.1% higher than SPFF, with an average anode current density of 7092.0 A·m⁻² and a current-density uniformity index of 0.135. The L-SFF also exhibits improved membrane water distribution, with an average water mole fraction of 0.513, and maintains an average membrane temperature of 356.61 K. Furthermore, the effect of GDL porosity on performance was evaluated. When the GDL porosity decreases from 0.8 to 0.2, the maximum power density decreases from 0.534 to 0.474 W·cm⁻² for SPFF (11.2%) and from 0.547 to 0.489 W·cm⁻² for L-SFF (10.6%), indicating better tolerance to variations in porous transport properties. The results demonstrate that the L-SFF structure enhances reactant transport, water management, and performance stability, providing an effective strategy for PEMFC flow-field optimization. Full article

This proposed study presents an in-situ experimental approach for investigating anode-side water dynamics through detailed analysis of pressure-drop data measured across multiple segments of bipolar plate flow channels. A theoretical model was first developed to predict pressure-drop profiles and liquid-water distribution in parallel multiphase flow channels. Based on the model, a bipolar plate with integrated multi-point pressure sensors was designed to enable real-time, spatially resolved monitoring of gas–liquid dynamics and provide advanced diagnostic capabilities. The proposed system was successfully implemented in a single Proton Exchange Membrane Fuel Cell (PEMFC) with an active area of 282 cm², which allows accurate identification of the timing and location of flooding events during operation. Experimental results showed that inadequate water management can cause flooding under steady-state conditions, while water accumulation is more prominent during startup and load-reduction processes. Additionally, the effects of reactant flow rate, humidity, and circulating water temperature on water distribution within the channels were systematically evaluated. Based on these findings, a closed-loop diagnostic and control framework is proposed by enabling dynamic adjustment of operating parameters according to pressure-drop characteristics for real-time optimization of water management and improved fuel cell performance. Full article

A series of fluorine-substituent-containing sulfonated poly(arylene ether) membranes was synthesized and evaluated as proton exchange membranes for fuel cell applications. Fluorinated difluoro monomers were first reacted with three different diol monomers through nucleophilic polycondensation to obtain 4FP4-series polymers, followed by controlled sulfonation to produce six S4FP4-series membranes with different ion exchange capacities and microphase-separated morphologies. FT-IR, ¹H-NMR, and ¹⁹F NMR spectroscopy confirmed the chemical structures of monomers, polymers, and sulfonated polymers. The resulting polymers exhibited good film-forming ability and high thermal stability. The sulfonated membranes showed ion exchange capacities of 1.74–2.80 mmol/g, water uptake of 24.7–116.3%, and favorable dimensional stability under elevated temperature. Most S4FP4 membranes exhibited proton conductivities higher than that of Nafion 211. In particular, S4FP4a (IEC of 1.74) achieved a proton conductivity of 262 mS cm⁻¹ at 80 °C and 95% RH and a maximum fuel cell power density of 1.07 W cm⁻², outperforming Nafion 211. TEM analysis revealed that fluorine substitution promoted effective microphase separation and continuous mesoscale aggregated domain. These results demonstrate that fluorinated sulfonated poly(arylene ether)s are promising candidates for high-performance proton exchange membranes. Full article

This study investigates machine learning techniques for predicting the behavior of proton exchange membrane electrolyzers, which are vital for sustainable hydrogen production. This work addresses these challenges by integrating artificial neural networks to develop predictive models capable of capturing the performance of proton exchange membrane electrolyzers with high accuracy. This research utilizes a multi-layer perceptron network architecture, optimized through rigorous data preprocessing, parameter tuning, and error minimization strategies. The dataset used was based on published PEME numerical simulation datasets and encompasses key performance indicators, including stack voltage, water transport, and electrochemical reactions. The trained artificial neural networks models achieved mean squared error values of 3.66 × 10⁻⁵ and 9.75 × 10⁻⁶, with correlation coefficients of 0.99996 and 0.99958, demonstrating near-perfect predictive accuracy. A comparative benchmarking study against alternative regression algorithms revealed that the proposed MLP models significantly outperformed Gradient Boosting and Random Forest by several orders of magnitude, thereby establishing a higher level of persuasiveness and reliability for the developed framework. Average deviation rates of 0.11% and −0.01% further validated model reliability. The novelty of this work lies in its comprehensive approach, which goes beyond isolated metrics by addressing interactions across system parameters. This integrated framework enables enhanced prediction, control, and optimization of proton exchange membrane electrolyzer’s performance, setting a new benchmark for leveraging machine learning in hydrogen energy systems. These findings pave the way for scalable, cost-effective solutions to improve proton exchange membrane electrolyzers’ efficiency and operational reliability. Full article

Electrochemical parameter identification is crucial for evaluating the electrochemical processes in proton exchange membrane water electrolysis (PEMWE). Conventional characterization techniques-including polarization-curve fitting, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current interruption (CI)-face significant limitations for rapid diagnostics under high-current dynamic operation, arising from constraints in instrument current rating, measurement time, zero-current control, and noise amplification in numerical differentiation. In this study, we present a simple current step (CS) method to accurately identify key electrochemical parameters and perform overpotential breakdown by using a simplified equivalent circuit model with a current source. To address the numerical instability in derivative calculation caused by sampling noise during voltage transient analysis, a physics-informed neural network (PINN) is introduced to enhance signal smoothness while guaranteeing physical consist ency. Compared with standard characterization, the proposed CS-PINN method demonstrates high accuracy, with an error of less than 2% in overpotential breakdown, less than 5.3% in ohmic resistance, and 2.8% in the Tafel slope (at 5 A/cm²). These results confirm that the CS-PINN method provides a fast, accurate, and equipment-friendly route for rapid electrochemical parameter identification in PEMWE. Full article

Proton exchange membrane fuel cell (PEMFC) parameter identification is a nonlinear computational modeling problem involving strongly coupled electrochemical parameters and partially unobservable polarization processes. Its experimental teaching is further constrained by the cost of fuel cell stacks and the safety requirements associated with hydrogen operation. To address these challenges, this study develops a computational modeling and intelligent simulation framework for a virtual teaching experiment on PEMFC parameter identification. A semi-empirical output-voltage model is established, and the sum of squared errors (SSE) between measured and simulated voltages is formulated as the optimization objective. An enhanced Logistic–Tent reverse snow ablation optimizer (LRSAO), termed RLFDB-LRSAO, is introduced by integrating roulette-wheel-selection-enhanced fitness-distance balance and Lévy flight perturbation. Its methodological novelty lies in the stage-wise coordination of population-diversity enhancement, candidate-selection guidance, and search perturbation within the LRSAO framework, rather than in the individual component strategies themselves. The framework organizes the identification process into mechanism interpretation, model construction, algorithm implementation, parameter configuration, visualization, comparative evaluation, and reflective analysis. Case studies using the NedStack PS6 and Modular SR-12 stacks yield best SSE values of 1.2173340 and 6.13503904, respectively. A small-scale qualitative teaching evaluation involving 20 postgraduate students indicated that the framework supported programming practice, strengthened conceptual understanding of PEMFC parameter identification and intelligent optimization, and provided useful support for research-oriented skills such as engineering problem analysis, technical writing, and innovation-oriented project development. These results provide preliminary evidence of technical and educational feasibility while supporting a cautious, problem-dependent interpretation of the optimizer. Full article