Analysis of operating parameters considering flow orientation for the performance of a proton exchange membrane fuel cell using the Taguchi method

This study has applied the L₁₈ 2 × 3⁷ orthogonal array of the Taguchi method to determine the optimal combination of six primary operating parameters (flow orientation, temperature of fuel cell, anode and cathode humidification temperatures, anode, and cathode stoichiometric flow ratios) of a PEM fuel cell. The optimal combination factor is co-flow, a cell temperature of 333 K, an anode humidification temperature of 353 K, a cathode humidification temperature of 333 K, a stoichiometric flow ratio for hydrogen of 2, and a stoichiometric flow ratio for oxygen of 3; and the amount of maximum power is 17.61 W. The results for the experiment indicate that flow orientation, temperature of fuel cell, and anode and cathode humidification temperatures are significant factors for affecting the performance. Furthermore, this study simulates the transport phenomenon and electrochemical reactions using a finite-element method at the optimal combination factor from the experimental results of Taguchi method.     

A novel computational approach to study proton transfer in perfluorosulfonic acid membranes

Density functional theory (DFT) calculations were done to obtain the energies of two perfluorosulfonic acid membranes at low humidity conditions. For the first time, an artificial neural network (ANN) approach along with statistical methods were employed for modeling, prediction, and analysis of the energies derived by the DFT method. The ANN method substantially does speed up the ab initio electronic structure calculations and has superior accuracy to mimic the results of such calculations. The designed ANNs for modeling the total and binding energies had high performance since the computed R² values were 0.99998 and 0.990, and the calculated root mean squared error (RMSE) values were 0.612173 Ha and 0.084901 Ha in predicting the total and binding energies, respectively. Statistical analysis of binding energies per water molecule using analysis of means (ANOM) and analysis of variance (ANOVA) methods showed that the hydration level has significant influence on the proton transfer in the perfluorosulfonic acid membranes. ANOM and ANOVA methods were also employed to determine the quantitative effect of other parameters (i.e., temperature and total charge of the system) as well as the combined effect of these parameters. The ease of the proton transfer was also assessed with the aid of the obtained potential energy surfaces.     

Electrolyzer-fuel cell combination for grid peak load management in a geothermal power plant: Power to hydrogen and hydrogen to power conversion

This study provides comprehensive energy, exergy, and economic evaluations and optimizations of a novel integrated fuel cell/geothermal-based energy system simultaneously generating cooling and electricity. The system is empowered by geothermal energy and the electricity is mainly produced by a dual organic cycle. A proton exchange membrane electrolyzer is employed to generate the oxygen and hydrogen consumed by a proton exchange membrane fuel cell utilized to support the network during consumption peak periods. This fuel cell can be also used for supplying the electricity demanded by the network to satisfy the loads at different times. All the simulations are conducted using Engineering Equation Solver software. To optimize the system, a multi-objective optimization method based on genetic algorithm is applied in MATLAB software. The objective functions are minimized cost rate and maximized exergy efficiency. The optimum values of exergy efficiency and cost rate are found to be 62.19% and 18.55$/h, respectively. Additionally, the results reveal that combining a fuel cell and an electrolyzer can be an effective solution when it comes to electricity consumption management during peak load and low load periods.     

Parametric analysis of the proton exchange membrane fuel cell performance using design of experiments

Proton exchange membrane fuel cell (PEMFC) performance depends on different fuel cell operating temperatures, humidification temperatures, operating pressures, flow rates, and various combinations of these parameters. This study employed the method of the design of experiments (DOE) to obtain the optimal combination of the six primary operating parameters (fuel cell operating temperatures, operating pressures, anode and cathode humidification temperatures, anode and cathode stoichiometric flow ratios). In the first stage, this study adopted a 2^k−2 fractional factorial design of the DOE to determine whether these factors have significant effects on a response and the interactions between various parameters. Second, the L₂₇(3¹³) orthogonal array of the Taguchi method is utilized to determine the optimal combination of factors for a fuel cell. Based on this study, the operating pressure, the operating temperature, and the interactions between operating temperature and operating pressure have a significant effect on the fuel cell performance. Among them, the operating pressure is the most important contributor. When the operating pressure increases, it should simultaneously lower the effects of other factors. While both the operating temperature and pressure increase simultaneously with that, the other factors are at appropriate conditions, it is possible to improve the fuel cell performance.     

Investigations on high performance proton exchange membrane water electrolyzer

In order to improve proton exchange membrane water electrolyzer (PEMWE) performance, some factors related to the processes of preparing the Membrane Electrode Assemblies (MEAs), such as iridium (Ir) electrocatalyst loading and Nafion^® content at the anode, thicknesses of proton exchange membrane and gas diffusion layers (GDLs), were examined. In addition, a home-made supported Ir/titanium carbide (Ir/TiC, 20% Ir by weight) was developed for the anode. With best commercial Ir catalyst loading of 1.5 mg cm⁻² Ir at the anode, the cell's current densities of 1346 mA cm⁻², 1820 mA cm⁻² and 2250 mA cm⁻² were achieved at the cell potentials of 1.80 V, 1.90 V and 2.00 V, respectively. A PEMWE with 0.3 mg cm⁻² Ir loading of Ir/TiC anode catalyst was comparatively stable and gave current densities of 840 mA cm⁻², 1130 mA cm⁻² and 1463 mA cm⁻² at the cell potentials of 1.80 V, 1.90 V and 2.00 V, respectively. Based on catalysis efficiency of Amperes per milligram of Ir, the Ir/TiC catalyst is found to be more active than unsupported Ir catalyst.     

Performance of a PEM electrolyzer using RuIrCoOx electrocatalysts for the oxygen evolution electrode

The Proton Exchange Membrane Water Electrolyzer (PEMWE) can be coupled to renewable energy sources (solar radiation and wave energy), which produce the necessary electricity for splitting the water. In this work the performance of a PEMWE using RuIrCoO_x as anodic electrocatalyst had been examined. The oxide powder was synthesized using a chemical reduction method, followed by thermal oxidation. The electrochemical properties of the electrocatalysts were examined by cyclical and lineal voltammetry in 0.5 M H₂SO₄. It was found that RuIrCoO_x oxide electrodes present a stable performance for OER. The PEMWE was designed and in-home built. Chrono-potentiometric experiments were recorded in the current range of 0.25 mA cm⁻² to 75 mA cm⁻² at 300 s. The current pulses length is chosen to be sufficiently long so that the voltage remains constant. Their intrinsic electrocatalytic activity in combination with their large surface area and stability are quite promising for the development of economically feasible electrocatalysts for (PEMWE).     

Prediction and analysis of the cathode catalyst layer performance of proton exchange membrane fuel cells using artificial neural network and statistical methods

A mathematical model was developed to investigate the cathode catalyst layer (CL) performance of a proton exchange membrane fuel cell (PEMFC). A numerous parameters influencing the cathode CL performance are implemented into the CL agglomerate model, namely, saturation and eight structural parameters, i.e., ionomer film thickness covering the agglomerate, agglomerate radius, platinum and carbon loading, membrane content, gas diffusion layer penetration content and CL thickness. For the first time, an artificial neural network (ANN) approach along with statistical methods were employed for modeling, prediction, and analysis of the CL performance, which is denoted by activation overpotential. The ANN was constructed to build the relationship between the named parameters and activation overpotential. Statistical analysis, namely, analysis of means (ANOM) and analysis of variance (ANOVA) were done on the data obtained by the trained neural network and resulted in the sensitivity factors of structural parameters and their mutual combinations as well as the best performance.     

The optimal design for PEMFC modeling based on Taguchi method and genetic algorithm neural networks

This paper has presented a new approach to estimate the output voltage of proton exchange membrane fuel cell (PEMFC) accurately by combining the use of a genetic algorithm neural networks (GANN) model and the Taguchi method. Using the PEMFC experimental data measured from performance test equipment of PEMFC, the GANN model could be trained and constructed for obtaining the steady state output voltage of PEMFC. Furthermore, in order to determine the important parameters in GANN, the Taguchi method is used for parameter optimization, with the goal of reducing the estimation error. The test equipment of PEMFC is accurate enough for acquiring the output voltage of PEMFC, and is quite useful for teaching purpose. However, taking the high cost, complicated operation procedure and environment safety into consideration, it is necessary to develop a simulation model of PEMFC to benefit teaching and R&D. Therefore, this paper will present an approach for constructing a GANN model with precise accuracy for the output voltage of PEMFC. For achieving the GANN model with high precision, a troublesome work has to be taken care of, that is, to determine all the parameters required in GANN. We will introduce Taguchi method to solve this problem as well. Finally, to show the superiority of proposed model, this approach has compared the estimation values of output voltage for PEMFC from GANN and BPNN models without using Taguchi method. One can easily find that the error of the proposed method is much smaller than that of the GANN model without Taguchi method and of the BPNN model; that is, the proposed approach has better performance on estimation for PEMFC output voltages.     

One-dimensional dynamic modeling of a high-pressure water electrolysis system for hydrogen production

Research on high-pressure water electrolyzers is under way worldwide as the economic production of hydrogen from renewable energy sources becomes more important. With increases in operating pressures, new safety issues have emerged, for which a reliable dynamic model of the electrolyzers is important for predicting their behavior. In this paper, a one-dimensional dynamic model of a high-pressure proton exchange membrane water electrolyzer is proposed. The model integrates various important physico-chemical phenomena inside the electrochemical cell that have been investigated individually into a dynamic model framework. Water transport, gas permeation, gas volume variation in anode/cathode channels, gas compressibility, and water vaporization are considered to formulate the model. Numerical procedures to handle and solve the model and the model performance for the prediction of steady and dynamic state behaviors are also presented.     

Geometry optimization and pressure analysis of a proton exchange membrane fuel cell stack

For proton exchange membrane fuel cells (PEMFCs), the distribution of reactant streams in the reactor is critical to their efficiency. This study aims to investigate the optimal design of the inlet/outlet flow channel in the fuel cell stack with different geometric dimensions of the tube and intermediate zones (IZ). The tube-to-IZ length ratio, the IZ width, and the tube diameter are adjusted to optimize the geometric dimensions for the highest pressure uniformity. Four different methods, including the Taguchi method, analysis of variance (ANOVA), neural network (NN), and multiple adaptive regression splines (MARS), are used in the analyses. The results indicate the tube diameter is the most impactive one among the three factors to improve the pressure uniformity. The analysis suggests that the optimal geometric design is the tube-to-IZ length ratio of 9, the IZ width of 14 mm, and the tube diameter of 9 mm with the pressure uniformity of 0.529. The relative errors of the predicted pressure uniformity values by NN and MARS under the optimal design are 1.62% and 3.89%, respectively. This reveals that NN and MARS can accurately predict the pressure uniformity, and are promising tools for the design of PEMFCs.     

Self-humidifying nanocomposite membranes based on sulfonated poly(ether ether ketone) and heteropolyacid supported Pt catalyst for fuel cells

In the present study, the self-humidifying nanocomposite membranes based on sPEEK and Cs_2.5H_0.5PW₁₂O₄₀ supported Pt catalyst (Pt-Cs_2.5H_0.5PW₁₂O₄₀ catalyst or Pt-Cs_2.5) and their performance in proton exchange membrane fuel cells with dry reactants has been investigated. The XRD, FTIR, SEM-EDXA and TEM analysis were conducted to characterize the catalyst and membrane structure. The ion exchange capacity (IEC), water uptake and proton conductivity measurements indicated that the sPEEK/Pt-Cs_2.5 self-humidifying nanocomposite membranes have higher water absorption, acid and proton-conductive properties compared to the plain sPEEK membrane and Nafion-117 membrane due to the highly hygroscopic and acidy properties of Pt-Cs_2.5 catalyst. The single cells employing the sPEEK/Pt-Cs_2.5 self-humidifying nanocomposite membranes exhibited higher cell OCV values and cell performances than those of plain sPEEK membrane and Nafion-117 membrane under dry or wet conditions. Furthermore, the sPEEK/Pt-Cs_2.5 self-humidifying nanocomposite membranes showed good water stability in aqueous medium. After investigation of several membranes such as sPEEK and sPEEK/Pt-Cs_2.5 membranes, the self-humidifying nanocomposite membrane with sulfonation degree of 65.12% for its sPEEK and 15 wt.% of catalyst with 1.25 wt.% Pt within catalyst was found to be the best proton exchange membrane for fuel cell applications. This self-humidifying nanocomposite membrane has a higher single cell performance than the Nafion-117 which was frequently used as a proton exchange membrane for fuel cell applications.     

Effects of temperature and humidity on the cell performance and resistance of a phosphoric acid doped polybenzimidazole fuel cell

Performance and electrochemical impedance spectroscopy (EIS) tests were performed at different temperatures and humidity levels to understand the effects of temperature and humidity on the performance and resistance of a PBI/H₃PO₄ fuel cell.The results of the performance tests indicated that increasing the temperature significantly improved the cell performance. In contrast, no improvement was observed when the gas humidity was increased. On the other hand, the EIS results showed that the membrane resistance was reduced for elevated temperatures. This development can be interpreted by the increase in membrane conductivity, as reflected by the Arrhenius equation. As the formation of H₄P₂O₇ and the self-dehydration of H₃PO₄ start around 130-140 °C, in PBI, they increase the membrane resistance at temperatures that are higher than 130 °C. In addition, the membrane resistance was reduced for elevated gas humidity levels. This is because an increase in humidity leads to an increase of the membrane hydration level.The resistance of the catalyst kinetics mainly contributes to the charge transfer resistance. However, under certain conditions, the interfacial charge transfer resistance is also important. It was concluded that the gas diffusion is the main contributor to the mass transfer resistance under dry conditions while it is the gas concentration under humid conditions.     

Dynamic modeling of a photovoltaic hydrogen fuel cell hybrid system

The objective of this paper is to mathematically model a stand-alone renewable power system, referred to as “Photovoltaic–Fuel Cell (PVFC) hybrid system”, which maximizes the use of a renewable energy source. It comprises a photovoltaic generator (PV), a water electrolyzer, a hydrogen tank, and a proton exchange membrane (PEM) fuel cell generator. A multi-domain simulation platform Simplorer is employed to model the PVFC hybrid systems. Electrical power from the PV generator meets the user loads when there is sufficient solar radiation. The excess power from the PV generator is then used for water electrolysis to produce hydrogen. The fuel cell generator works as a backup generator to supplement the load demands when the PV energy is deficient during a period of low solar radiation, which keeps the system's reliability at the same level as for the conventional system. Case studies using the present model have shown that the present hybrid system has successfully tracked the daily power consumption in a typical family. It also verifies the effectiveness of the proposed management approach for operation of a stand-alone hybrid system, which is essential for determining a control strategy to ensure efficient and reliable operation of each part of the hybrid system. The present model scheme can be helpful in the design and performance analysis of a complex hybrid-power system prior to practical realization.     

Hybrid artificial bee colony algorithm for parameter estimation of proton exchange membrane fuel cell

The accurate electrochemical model plays an important role in design and analysis of hydrogen fuel cell systems. For the purpose of estimating parameters of the proton exchange membrane fuel cell (PEMFC) model, and inspired by the foraging behavior of bacteria and bees, a hybrid artificial bee colony (HABC) algorithm is proposed. The HABC uses an improved solution search equation that mimics the chemotactic effect of bacteria to enhance the local search ability. To avoid premature convergence and improve search accuracy, the adaptive Boltzmann selection scheme is adopted, which adjusts selective probabilities in different stages. Performance testing has been conducted on some typical benchmark functions. The results demonstrate that the HABC outperforms other methods (BIPOA, PSOPS and two improved GAs) in both convergence speed and accuracy. The proposed approach is applied to estimate the PEMFC model parameters and the satisfactory model predictive curves are obtained. More experimental results in different search ranges and validate strategies indicate that HABC is an efficient technique for the parameter estimation problem of PEMFC.