Fuel Cells最新文献

Attaining sustainability in energy systems is a critical task in confronting global environmental issues. Hydrogen fuel cells, especially proton exchange membrane fuel cells (PEMFCs), present a viable avenue for clean and efficient energy solutions. Precise identification of the characteristics influencing PEMFC models is crucial for improving their effectiveness, reliability, and flexibility in real-world applications. This paper presents a unique optimization method, the Flood Algorithm (FLA), for effective and accurate parameter determination in PEMFC models. The FLA, influenced by natural flood dynamics, incorporates mathematical models of essential physical phenomena, including water flow on inclines, temporal flow rate variations, soil permeability, and water level changes induced by precipitation and evaporation. These concepts direct the algorithm toward global optimization by methodically balancing exploration and exploitation. The FLA functions through two principal phases: a regular movement phase that guarantees convergence and a flooding phase that promotes diversification to circumvent local optima. The proposed methodology is confirmed by experimental data from four commercial PEMFC stacks: 250 W, H-12, BCS 500 W, Temasek, and SR-12 by minimizing the sum of squared errors (SSE). The optimal SSE values of 0.624709, 0.096533, 0.0115561, 0.117086, and 1.056369779 were attained, indicating enhanced accuracy relative to contemporary metaheuristic algorithms and extensively cited methodologies in the literature. The findings highlight the resilience and effectiveness of the FLA in achieving accurate PEMFC parameter estimation, supported by comparisons of SSE and statistical indicators.

Proton Exchange Membrane Fuel Cells (PEMFCs) play a crucial role in energy transition owing to their efficiency and eco-friendly nature. However, precise parameter estimation plays a critical role in effective modeling and control of PEMFC systems. In this study, the enhanced artificial bee colony (E-ABC) algorithm is proposed for the accurate estimation of model parameters. The random scout phase of the ABC algorithm is replaced by a guided search using sine and cosine functions to enable more efficient and balanced exploration of the solution space in the developed approach. Two commercial PEMFC stacks, BCS 500 W and NedStack PS6, are considered for performance evaluation. The effectiveness of the proposed E-ABC algorithm in PEMFC parameter estimation was analyzed through comparison with several algorithms. E-ABC successfully estimated the parameters of the BCS 500 W and NedStack PS6 PEMFC systems with sum of squared errors values of 0.01191 and 2.14895, respectively. Results from comparative analysis clearly indicate that the E-ABC algorithm outperforms other methods by achieving significantly lower estimation errors. Reduced median and standard deviation values serve as evidence of greater performance stability and consistency. Also, nonparametric statistical analyses further verify the statistical significance of the superiority of the proposed E-ABC algorithm.

This study reports the development of a self-sustained single-chamber microbial fuel cell (SCMFC) biosensor integrated with artificial intelligence (AI) for rapid assessment of heavy metal toxicity in water. The anode was modified with a conductive and biocompatible polyindole/titanium oxide/molybdenum oxide (PIn/TiO₂/MoO₃) nanocomposite to enhance electron transfer and microbial attachment. The biosensor was evaluated against four common toxic metals Hg²⁺, Cr⁶⁺, Cu²⁺, and Ni²⁺ at concentrations ranging from 2 to 20 mg/L. Concentration-dependent inhibition of microbial activity was observed, with mercury exhibiting the highest toxicity (88.88% inhibition at 20 mg/L), followed by chromium (81.81%), copper (71.42%), and nickel (27.77%). A hybrid Artificial Neural Network–Genetic Algorithm (ANN–GA) model was employed to predict toxicity based on inhibition ratios, achieving superior accuracy (R² = 0.998; RMSE = 0.262 mg/L) compared to baseline ANN models (R² = 0.974). The biosensor demonstrated excellent reusability through regeneration using sodium acetate (1 g/L), with complete voltage recovery (0.01–0.35 V) achieved within 70–110 h depending on metal type. These results establish the PIn/TiO₂/MoO₃@Toray carbon modified SCMFC as a robust, cost-effective, and AI-enhanced biosensing platform capable of real-time, quantitative monitoring of heavy metal toxicity in wastewater.

Green energy and renewable energy sources (RESs) are between the most important topics in power, energy, and transportation and are crucial for sustainability for next generations. When a hydrogen fuel cell or solar array is used for the electric vehicle (EV), the next step is using an efficient, high power, and simple structure based power electronics converter to storage the energy of these RESs into the battery pack. The integration of artificial intelligence into the control and optimization of DC–DC power converters presents promising opportunities in improving energy management and efficiency in EV sector. This study presents a low-input current and low voltage stress topology for application in fuel cell to battery charging systems in EVs. A current filter by forming a switched inductor cell at the input side of the converter guarantees a small ripple for input sources that enhances the longevity and long-life of the FC stacks or solar panels. The application of the switched capacitor circuits at the input and end sides of the converter decreases the voltage stress across the semiconductor devices and enhances the mean time to failure rate of the converter that is between the most important features of a converter. The presented topology enhances the input voltage to 7 and 19 times for duty ratios equal to 0.5 and 0.8, respectively, while the switch experiences three and nine times the input voltage for the same duty ratios, which is considerable. The configuration of the diodes and capacitors in the switched capacitor, by dividing the total voltage stress, results in impressively low voltage ripples. This converter includes one power switch, which minimizes the complexity of the controller and enhances the feasibility. The converter design incorporates a three-layer, three-input artificial neural network structure. The regression values were 0.982 for training, 0.983 for testing, and 0.9827 overall, indicating minimal prediction error and confirming the effective training of the neural network model. The laboratory test results for power levels around 200 W have been presented and confirm the correctness of the proposed algorithm and the application of the proposed converter. To obtain 0.5 A for the load under a 350 VDC output voltage, the input source presents an average current equal to 8 A, and the switch experiences around 160 V voltage stress across the drain–source pins. Results show that Inductor L₂ has lower current stress than Inductor L₁.

This study extends our previous work on thin metal-supported solid oxide fuel cells (MS-SOFCs) fabricated via atmospheric plasma spraying (APS), focusing on enhancing stack performance through an innovative interconnect (IC) design. The newly developed IC configuration—featuring discrete cylindrical ribs and a hybrid flow field—significantly improved fuel and air utilization, reduced contact and polarization resistances, and enhanced thermal-mechanical stability. Compared to conventional straight-channel ICs, the new design increased single-cell fuel utilization from 38% to 53% and improved five-cell stack power output from 158 to 205 W at 0.8 V. Furthermore, four 36-cell kilowatt-class stacks exhibited excellent reproducibility, delivering power outputs above 1.2 kW at 750°C. Integration into a 3.5 kW SOFC system using methane steam reformate yielded stable operation with > 36% electrical efficiency. These results demonstrate that the proposed IC design not only boosts electrochemical performance but also facilitates scalable system-level implementation of thin MS-SOFCs.

Microbial fuel cells (MFCs) are grabbing attention as a sustainable technology for concurrent wastewater treatment and bioelectricity generation. The anode plays an active role in this system by supporting bacterial attachment, facilitating substrate oxidation, and enabling electron transfer. This review presents an updated assessment of recent advances in anode materials. The particular emphasis is on biomass-derived carbons, graphene-based composites, and emerging hybrid electrodes. The discussion highlights how surface chemistry, porosity, conductivity, and structural modifications influence biofilm formation and electrochemical behavior. It further discusses the key challenges related to cost, durability, and scale-up, alongside the environmental implications of using nanomaterials for enhancing the MFCs' competence. The review also outlines future directions for improving material design, understanding microbial-electrode interactions, and evaluating performance under real wastewater conditions. These insights provide a foundation for developing practical, cost-effective anodes that support the wider operation of MFCs technology.

Effective control of air supply subsystem is key to performance of the proton exchange membrane fuel cell stack. This paper proposes a sensitivity analysis-based coordination control algorithm for air supply subsystem of a 140 kW fuel cell stack. Sensitivity of mass flow and pressure to throttle angle and rotation speed of air compressor is analyzed based on dynamic model of the subsystem. A feedforward and feedback-based coordination control algorithm is constructed based on individual algorithm deduced from sensitivity analysis. Rules are given for determination of parameters of feedback controller. Qualitative analysis of the control algorithm with appropriate limit on actuators shows that air pressure and mass flow can be limited to an acceptable domain around target point. Experiment on air supply subsystem for a 140 kW fuel cell stack validates that relative error of air pressure and mass flow is all controlled within ±1% at rated current of 697 A in steady state. Overshoot of mass flow and pressure is 1.6% and 1.2%, respectively, for load change from 88 to 697 A and undershoot of them −1.4% and −1.1%, respectively, from 697 to 88 A. Dynamic trajectory of mass flow and pressure validates the qualitative analysis.

Protonic ceramic fuel cells (PCFCs) have high energy conversion efficiencies and can be employed as next-generation fuel cells. However, novel cathode materials with high electrochemical performances at low operating temperatures are required to practically apply PCFCs. BaCo_0.4Fe_0.4Zr_0.1Y_0.1O_3–δ (BCFZrY) is a promising material for PCFC cathodes owing to its low electrode polarization resistance (R_p). Herein, instead of Zr doping, BaCo_0.4Fe_0.4A_0.1Y_0.1O_3–δ (BCFAY) compounds, where A represents Ti, Mg, or Zn, were evaluated as PCFC cathode materials. BCFAY (A: Ti, Mg, or Zn) compounds have a higher oxygen permeability than BCFZrY. The electrochemical properties and power generation performance of the PCFCs with BCFAY cathodes were measured and compared with those of PCFCs with BCFZrY cathodes. The PCFC with a BCFMgY cathode exhibited the lowest R_p, leading to the highest power density, that is, 0.80 W cm⁻² at 600°C. In addition, for all PCFCs with BCFY-based cathodes, the reciprocal area-specific resistances of R_p and oxygen permeability fluxes of the cathode materials had an approximately proportional relationship. Therefore, it was confirmed that PCFC cathodes with a high oxygen permeability had a low cathode polarization resistance, which significantly enhanced the power generation performance of PCFCs.

To capture the dependence of proton exchange membrane fuel cell (PEMFC) behavior on both operating conditions and intrinsic parameters, this study develops a quasi-two-dimensional electrochemical impedance spectroscopy (EIS) model that incorporates gas transport, charge transfer, and interfacial double-layer capacitance on both anode and cathode sides. The model reproduces typical impedance arc characteristics and enables frequency-domain separation of loss mechanisms: ultra-high, high, medium, and low frequency regions are governed by ionic conductivity, anode electrochemical kinetics, cathode electrochemical kinetics, and oxygen transport parameters, respectively. A wide range of external and intrinsic parameters are analyzed, including load, stoichiometric ratio, humidity, ionic conductivity, double-layer capacitance, charge transfer coefficients and oxygen diffusion coefficient. Among them, increasing cathodic double-layer capacitance enlarges and shifts the mid- to low-frequency arcs due to charge accumulation, while anodic capacitance mainly affects the high-frequency arc and its separation from cathodic kinetics. In addition, membrane electrode diffusion primarily affects the magnitude of the mass transfer arc, while flow channel transport mainly influences its characteristic frequency; together, they respectively determine the oxygen transport resistance and the transport timescale. These findings demonstrate that the model captures experimentally observed EIS behaviors and provides a framework for diagnosing performance-limiting factors, thereby supporting the optimization of PEMFC operation and durability.

The global demand for proton exchange membrane fuel cells (PEMFCs) is rapidly increasing due to their high efficiency, zero carbon emissions, and potential for sustainable energy conversion in transportation and power systems. Compared to internal combustion engines, gas engines, and turbine systems, PEMFCs demonstrate superior energy efficiency. However, achieving consistent and sustainable efficiency remains a key research challenge, driving numerous ongoing investigations. One crucial factor affecting PEMFC performance is heat management, as it significantly influences microscopic and macroscopic particle behavior near and surface of electrode. The present study focuses on analyzing the scattering dynamics of charge particle on the electrode surface (thermal electron) due to surrounding particles (thermal hydrogen) and their impact on PEMFC performance. The research comprises both theoretical and experimental approaches. Theoretically, a relationship was developed between the differential cross-section (DCS) and various PEMFC parameters, whereas the experimental work aimed to verify the proposed model. The results indicate that DCS decreases with increasing current density, whereas temperature rises with increasing output current. DCS also decreases with the scattering angle, reaches a minimum, and then increases again at different output voltages and efficiencies. Similarly, DCS increases with incident electron energy, attains a maximum, and then decreases as the energy continues to rise. In regions of smaller separation, DCS increases with separation at different voltages and efficiencies. Experimental results confirm that rising temperature reduces the cell's output voltage, verifying the theoretical inverse relationship between DCS and voltage. These findings contribute to enhancing PEMFC performance, supporting the application of advanced technologies such as scanning tunneling microscopy, laser-induced fluorescence, quantum computing, and nanophotonic sensors for improved efficiency and control.