Fuel Cells最新文献

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.

The stabilized Bi₂O₃ electrolyte bilayer solid oxide fuel cells (SOFCs) are known as promising intermediate temperature SOFCs. However, it is necessary to develop a cost-effective method for manufacturing electrolyte bilayer SOFCs. In this study, atmospheric plasma spraying (APS) is employed to develop a facile method to deposit EWSB ((Bi₂O₃)_0.705(Er₂O₃)_0.245(WO₃)_0.05) and ScSZ ((Sc₂O₃)_0.1(Zr₂O₃)_0.9) electrolytes for assembling SOFCs with an EWSB/ScSZ bilayer structure. Results show that the maximum power density (MPD) of the electrolyte bilayer cell with 20 µm EWSB is increased by 52% compared with the monolayer ScSZ electrolyte cell at 750°C. The cell of electrolyte bilayer with a densified ScSZ presents open circuit voltage of ∼1 V and a remarkable performance enhancement with the MPDs of 1110 mW cm⁻² at 750°C and 581 mW cm⁻² at 650°C, being increased by 57% at 650°C compared with electrolyte bilayer cell with the as-sprayed ScSZ electrolyte. The dense ScSZ electrolyte effectively ensures the superior electrochemical performance and stability of EWSB at the interface between electrolytes of EWSB/ScSZ bilayer cell.

Nanofluids exhibit higher thermal performance than conventional fluids and are preferred as cooling fluids in thermal management of polymer electrolyte membrane (PEM) fuel cells. In order for a nanofluid to be used in PEM fuel cell cooling, it should have high stability, high heat removal performance, and low electrical conductivity (EC). In this study, the utilization of Fe₃O₄-water nanofluid in PEM fuel cell cooling was investigated using a novel technique that considered all three of these features into account. The nanofluid was synthesized in varying mass ratios of 0.1%–0.5% and its thermophysical properties, EC, and zeta potential were measured. According to the findings, when EC and stability were taken into account, the pH value of the Fe₃O₄-water nanofluid should exceed 7. The thermal performance of the nanofluids was assessed using the performance evaluation ratio (PER), Mouromtseff number (Mo), and h_r under both laminar and turbulent flow conditions. A maximum heat transfer improvement of 19% for laminar and 18% for turbulent flow conditions was achieved at a mass ratio of 0.4%. In addition, an artificial neural network (R² = 0.9999, MSE = 0.000944) was used to model the EC. For the first time in the literature, a correlation was proposed to predict the EC of a nanofluid on the basis of pH and mass ratios.

The proton exchange membrane fuel cell (PEMFC) employs lightweight metallic bipolar plates (BPP) with a 0.2 mm thickness, offering a sustainable and recyclable energy solution. These plates are essential for distributing gases through flow channels, conducting electricity, and managing heat transfer while balancing cost-efficiency, lightweight properties, and durability for practical applications. Electromagnetic forming (EMF) is a high-speed, noncontact manufacturing technique that ensures uniform pressure distribution without lubricants and uses a single coil and power supply to produce BPP with intricate patterns, enabling the creation of complex, sharp-edged components with precision. This study investigates the impact of key parameters, such as discharging voltages (10 000, 11 000, and 12 000 V) and capacitor bank energy levels, using a 25 000 J EMF machine to fabricate copper-based BPP. Copper's high conductivity supports magnetic fields, Lorentz forces, and eddy currents, which are critical as electric currents flow through the workpiece during EMF. A novel EMF-based approach is also introduced to manufacture metallic BPP with superior quality and dimensional accuracy in flow field channels, offering significant advantages over traditional methods. This innovative technique, leveraging the unique benefits of EMF, will be discussed in detail, highlighting its potential to transform BPP production for enhanced efficiency and performance.