Fuel Cells最新文献

The reliability and efficiency of proton exchange membrane fuel cells largely depend on the performance of the air supply system, making high-control accuracy essential. First, the impact of control accuracy on the energy consumption and efficiency of the air compressor is analyzed. Subsequently, a fuel cell system model is established based on experimental data to enable rapid verification of control strategies. Finally, three decoupling control algorithms (feedforward decoupling, feedback decoupling, and diagonal matrix decoupling) are compared in detail. The results show that the diagonal matrix decoupling algorithm has higher stability and minimizes the coupling between pressure and flow. Experimental verification on the fuel cell system test bench further shows that the diagonal matrix decoupling algorithm can limit the flow and pressure fluctuations to less than 0.5 g/s and 0.5 kPa, respectively, and effectively prevent compressor surge during startup. This method provides theoretical guidance for achieving high-precision control of the air supply system of fuel cell vehicles.

The article explores the various types of solvents and the water content of the ink. It also considers whether dispersion is requisite, the size and weight of zirconia balls during dispersion, the influence of ball milling time and speed on the quality of the catalyst layer, and the determination of the solvent system consisting of n-propanol, 80% water content, and 1 micrometer (µm) zirconia balls, with two equivalents. A formula and method are devised with a rotating speed of 2000 revolutions per minute (rpm) and a duration of 20 min, respectively, to create a crack-free catalyst layer. Through an investigation of the effect of the ionomer content in the formula on performance, it is clarified that a closer bond between the catalyst layer and the proton exchange membrane emerges. This is because the ionomer in the ink shares the same molecular structure with the membrane. Consequently, the catalyst layer becomes more porous, reducing the mass transfer resistance and ohmic resistance under standard operating conditions, resulting in a performance of [email protected]/cm² for direct coating. However, it also leads to a reduction of the ionomer on the side of the catalyst layer away from the proton exchange membrane.

Hybrid energy systems that integrate renewable energy sources are driving the green energy revolution and playing an increasingly vital role in supporting sustainable transportation through electric vehicle charging infrastructure. This study involves the meticulous design of a reliable standalone multi-vector hybrid energy configuration comprising photovoltaic panels, wind turbines, and fuel cells (PV/WT/FC) for stochastic electric vehicle (EV) load. Significantly, the research presents a pioneering methodology that incorporates chaotic particle swarm optimization aligned with the Andean Condor algorithm (CPSO-ACA), providing a sophisticated optimization approach. The evaluation process is based on key measures like net present cost (NPC), levelized cost of energy (LCOE), and reliability indicators such as loss of load probability (LOLP), loss of load expectation (LOLE), and loss of energy expected (LOEE). With the proposed hybrid approach, a reliable hybrid energy system with the lowest renewable energy components and promising reliability (LOLP = 0.064) has been reported. From a financial perspective, the values of NPC, LCOE, and LOE ($4.06 M, $0.0636/kWh, and $0.7083 M) enable the hybrid system to be economically sound. Furthermore, the energy-oriented reliability indices, LOEE and LOLE, have significantly reduced to 5920 kWh and 564.144 h, respectively. The effectiveness of the proposed algorithm is compared with GA, GWO, MOPSO, and CPSO algorithms and is indicative of the strength achieved through proposed optimization in the evolving landscape of green energy technology.

Ensuring uniform fluid distribution in high-power fuel cell stacks is crucial for automotive applications. This study introduces and evaluates novel X1- and X2-shaped manifold designs against the conventional U-shaped manifold to enhance distribution uniformity across cells. Computational Fluid Dynamics simulations demonstrated the superiority of the proposed designs, with the X2 manifold exhibiting improved pressure uniformity and reduced pressure drop due to its double-inlet configuration. Further optimization was conducted using a multi-objective genetic algorithm and topology optimization techniques, refining the flow area for enhanced performance. Results indicated that reducing the inlet size while maintaining the outlet size significantly improved gas distribution across all manifold configurations. Additionally, integrating a C-type inlet pipe in the X2 manifold further enhanced flow consistency and reduced manifold size by 50 percent. These findings highlight the effectiveness of advanced computational and optimization strategies in fuel cell manifold design, offering practical solutions to enhance flow distribution and overall stack performance.

Proton exchange membrane fuel cells (PEMFCs) are emerging as a promising alternative power source, converting hydrogen and oxygen into clean energy. Accurate mathematical modeling of PEMFCs is essential for their simulation, evaluation, optimization, and effective management. This study introduces a newly developed metaheuristic algorithm, the Horned Lizard Defense Tactics Optimization Algorithm (HLDTOA), for parameter identification in PEMFC mathematical models, leveraging semi-empirical equations to enhance precision. The HLDTOA is applied to determine the unknown design parameters of various PEMFCs under diverse operating conditions of pressure and temperature. The HLDTOA achieved a 37.23% improvement in min sum of squared error (SSE) (0.64193093 opposed to 1.0227417), an 18.37% improvement (0.09653342 compared to 0.11827), a 3.32% improvement (1.05636977 compared to 1.0926766), and a 27.32% improvement (1.50432678 opposed to 2.07) for H-12, 250 W PEMFC SR-12, for Nedstack, respectively. Statistical analyses further demonstrate the robustness and superiority of HLDTOA. The high correlation between derived and experimentally measured I–V polarization curves underscores its precision and reliability. Additionally, the dynamic characteristics of PEMFCs are evaluated to test the optimized parameters under varying reactant pressures and cell temperatures. The HLDTOA offers exceptional accuracy and reliability in identifying unknown PEMFC parameters, marking a significant advancement in fuel cell modeling and optimization.

In the present study, a strongly coupled three-dimensional thermal-electric-fluid-mass model was developed, and the thermal-electric performance of the solid oxide fuel cell (SOFC) with a zigzag channel under various operating conditions was analyzed. The results indicated that increasing the operating temperature and the anode inlet Reynolds number could enhance the output power density of the SOFC, whereas the temperature gradient within the SOFC also increased accordingly. The enhancement of these parameters led to an increase in the electrical performance (characterized by power density) of the SOFC while concurrently diminishing its thermal performance (characterized by temperature gradient). Under the same conditions, the SOFC with a zigzag channel exhibited superior electrical performance compared to the SOFC with a conventional parallel channel, albeit with slightly inferior thermal performance. Keeping the flow parameters constant (Re = 1.0) and the temperature maintained at 1123 K, the electrical performance of the SOFC with a zigzag channel was 8.6% higher than that of the SOFC with a parallel channel, whereas the thermal performance was 4.2% lower. Keeping the temperature parameter constant (T = 1073 K) and the anode inlet Reynolds number maintained at 1.7, the output power density of the SOFC with a zigzag channel was 5.9% higher than that of the SOFC with a parallel channel, whereas the temperature uniformity was 2.6% lower. The issue of internal temperature non-uniformity caused by the zigzag channel design of the SOFC could be balanced by adopting the co-flow operation condition. At a working temperature and flow condition of T = 1073 K and Re = 1.0, the thermal performance of the SOFC with a zigzag channel in a co-flow configuration was 5% higher than that in a counter-flow configuration, whereas its electrical performance decreased by only 0.2% compared to the counter-flow configuration.

Proton exchange membrane fuel cells (PEMFC), as an important part of clean energy technology, are widely used in transport, portable power sources and stationary power systems. PEMFC experience aging during use, resulting in degradation of their performance and shorter lifespan. In this paper, a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), informer, and long short-term memory (LSTM) is proposed to predict the aging trend. The data are decomposed into multiple Intrinsic Mode Function (IMF) through CEEMDAN, which are reconstructed according to sample entropy (SE) to provide stable data for the model. A new prediction approach is proposed to predict informer and LSTM in parallel while extracting multifaceted features. Different datasets, different training stopping points (TSP), and multiple models are used to validate the accuracy and stability of the model. The root mean square error (RMSE) and mean absolute error (MAE) can reach 0.00137 and 0.00060 for the steady state dataset, and the prediction is better for the quasi–dynamic dataset with RMSE and MAE reaching 0.00126 and 0.00065.