Numerical study of thermal stresses in high-temperature proton exchange membrane fuel cell (HT-PEMFC)

The purpose of this work is to numerically examine the thermal stress distributions in a high-temperature proton exchange membrane fuel cell (HT-PEMFC) based on a phosphoric acid doped polybenzimidazole (PBI) membrane. A fluid structure interaction (FSI) method is adopted to simulate the expansion/compression that arises in various components of a membrane electrode assembly (MEA) during the HT-PEMFC assembly processes, as well as during cell operations. First, three-dimensional (3-D) finite element method (FEM) simulations are conducted to predict the cell deformation during cell clamping. Then, a nonisothermal computational fluid dynamic (CFD)-based HT-PEMFC model developed in a previous study [1] is applied to the deformed cell geometry to estimate the key species and temperature distributions inside the cell. Finally, the temperature distributions obtained from these CFD simulations are employed as the input load for 3-D FEM simulations. The present numerical study provides a fundamental understanding of the stress–temperature interaction during HT-PEMFC operations and demonstrates that the coupled FEM/CFD HT-PEMFC model presented in this paper can be used as a useful tool for optimizing HT-PEMFC clamping and operating conditions.     

Numerical analysis of high-temperature proton exchange membrane fuel cells during start-up by inlet gas heating and applied voltage

This paper investigates the start-up or warm-up process of a high-temperature proton exchange membrane fuel cell (HT-PEMFC) from room temperature to a desired temperature of ～180 °C. The heating strategy considered in this study involves an initial heating of the HT-PEMFC by a process referred to as inlet gas heating to a temperature above 100 °C. After the fuel cell reaches above 100 °C, a voltage is applied, where electrochemical reaction heating is expected to contribute to the heating process. Thus, a numerical transient non-isothermal three-dimensional model is derived to mimic the start-up process. Operational parameters such as anode inlet temperature, cathode inlet temperature, applied voltage and voltage application temperature are varied and their effects on the maximum temperature in the membrane electrode assembly (MEA) and temperature difference in the MEA are studied. Firstly, the distribution of temperature along the channel length indicates an increase of temperature during gas heating and as the voltage is applied at the voltage application temperature, the temperature increases at the centre of the MEA due to exothermic reactions. The two-dimensional temperature distribution indicates a temperature difference between the centre of the MEA and the regions below the bipolar plate where the temperature is relatively lower. Considering the whole start-up process with respect to time, the temperature difference exists throughout the process. This will be the key focus in the parametric study. The parametric study indicates that the inlet gas temperatures, applied voltage and the voltage application temperature affect the maximum temperature in the MEA and most importantly, the temperature difference in the MEA. This can cause thermal stresses to build-up if the increase rate of temperature difference is excessive. Setting the applied voltage high (thus, lower current density) is necessary to reduce the increase rate of temperature difference.     

Analytical modelling of boiling phase change phenomenon in high-temperature proton exchange membrane fuel cells during warm-up process

This paper investigates the thermal and water balance as well as the electro-kinetics during the warm-up process of a Hydrogen/Oxygen high-temperature proton exchange membrane fuel cell (HT-PEMFC) from room temperature up to the desired temperature of 180 °C. The heating strategy involves the extraction of constant current from the fuel cell, while an external heating source with a constant heat input rate is applied at the end plates of the cell simultaneously. A simple analytical unsteady model is derived addressing the boiling phase changing phenomenon in the cathode catalyst layer (CCL) and cathode gas diffusion layer (CGDL) of the cathode that occurs when the temperature of the fuel cell reaches the boiling temperature of water. Parameters such as the heat input rate, extracted current, cathode pressure and cathode stoichiometric flow ratio are varied and their effects on the temperature, liquid water fraction and most importantly, the voltage profiles with respect to time, are explored. A comparison between other existing heating strategies using the model suggests that there is insignificant improvement in warm-up time when current is extracted from room temperature considering a single cell. However, considering the solution for a typical 1-kW stack suggests that reductions in warm-up time and energy consumption can be expected. In addition, the results show that boiling phase change is found to be a key factor that affects the level of water saturation in the porous media such as the CCL and CGDL during the warm-up process, when current is extracted from the start of the process i.e. room temperature. However, the energy consumption due to boiling phase change is found to be negligible as compared to external heating input rate. The parametric studies show that the variation of heat input rate, extracted current and cathode pressure have significant effect on the cell voltage that is strongly dominated by the liquid water fraction in the porous media. On the other hand, the variation of cathode stoichiometric flow ratio is found to have minimal effect on the output cell voltage. The parametric studies also indicate that boiling phase change is present for a significant period of time under typical operating conditions.     

Real-time data-driven fault diagnosis of proton exchange membrane fuel cell system based on binary encoding convolutional neural network

The performance of proton exchange Membrane fuel cell (PEMFC) fault diagnosis system plays an important role in normal operation of PEMFC. Therefore, a new fault diagnosis algorithm based on binary matrix encoding neural network called BinE-CNN is proposed. In BinE-CNN, high-dimensional features are extracted through binary encoding, and the feature maps are transferred to a convolutional neural network (CNN) to realize seven-category fault classification. For development of BinE-CNN, a PEMFC model is modeled to generate simulative datasets. Simulative test precision and Frames per second (FPS) of BinE-CNN have reached respectively 0.973 and 999.8 (better than support vector machines (SVM), long short-term memory neural network (LSTM), etc.). In experimental verification section, fault datasets are collected during bench test. After that, BinE-CNN is deployed on vehicle control unit (VCU) to verify its engineering value (real-time and precision). The result meet both requirements, with time cost of 96.15 ms and precision of 0.931.     

A facile method of asymmetric ether-containing polybenzimidazole membrane for high temperature proton exchange membrane fuel cell

A facile method has been suggested for the preparation of poly [2,2′-(p-oxydiphenylene)-5,5′-benzimidazole] (OPBI) membrane that comprises of dense and porous layers for high temperature proton exchange membrane fuel cells (HT-PEMFCs). The porosity as well as the asymmetry of the membrane is formed by the use of component solvent at steadily increasing temperatures. This approach needs not to use any porogen, whereas the resultant asymmetric OPBI membrane indicates an improved phosphoric acid (PA) doping level together with mechanical strength. For instance, the PA doping level of the asymmetric OPBI membrane is almost twice as much as that of the homogenous dense OPBI membrane. The conductivity of asymmetric OPBI arrived at 0.072 S cm^?1 at 180 °C. The membrane electrode assembly (MEA) based on the asymmetric OPBI demonstrated an exceptional fuel cell functionality with a peak power density of 393 mW cm^?2 at 160 °C under anhydrous conditions.     

Three-dimensional two-phase flow model of proton exchange membrane fuel cell with parallel gas distributors

A non-isothermal, steady-state, three-dimensional (3D), two-phase, multicomponent transport model is developed for proton exchange membrane (PEM) fuel cell with parallel gas distributors. A key feature of this work is that a detailed membrane model is developed for the liquid water transport with a two-mode water transfer condition, accounting for the non-equilibrium humidification of membrane with the replacement of an equilibrium assumption. Another key feature is that water transport processes inside electrodes are coupled and the balance of water flux is insured between anode and cathode during the modeling. The model is validated by the comparison of predicted cell polarization curve with experimental data. The simulation is performed for water vapor concentration field of reactant gases, water content distribution in the membrane, liquid water velocity field and liquid water saturation distribution inside the cathode. The net water flux and net water transport coefficient values are obtained at different current densities in this work, which are seldom discussed in other modeling works. The temperature distribution inside the cell is also simulated by this model.     

A data-driven digital-twin model and control of high temperature proton exchange membrane electrolyzer cells

The high temperature proton exchange membrane electrolyzer cells (HT-PEMEC) are promising for hydrogen generation from fluctuating and intermittent renewable energy. In this study, a data-driven method is developed to study the dynamic behavior of HT-PEMEC. This method combines multiphysics simulation and nonlinear system identification, avoiding expensive experimental costs and time-consuming full multiphysics calculations. Dynamic models for predicting the power consumption, hydrogen production and temperature are identified, and the verified fit is 96.31%, 97.87%, 87.73%, respectively, which demonstrated the accuracy of the identification model. Subsequently, the identification model was used to predict the dynamic behavior of HT-PEMEC and design control strategies. Fuzzy control strategy and neural network predictive control strategy are implemented to alleviate overshoot and suppress fluctuations so as to improve the durability of the electrolyzer. Moreover, compared with the fuzzy control strategy, the neural network predictive control strategy reduces the power overshoot by approximately 92%. This data-drive digital-twin model can not only guide dynamic experimental research, but also can be extended to study the dynamic behavior of various fuel cells and electrolyzer cells.     

Performance analysis of a proton exchange membrane fuel cell using tree-shaped designs for flow distribution

The performance of a proton exchange membrane (PEM) fuel cell is directly associated to the flow channels design embedded in the bipolar plates. The flow field within a fuel cell must provide efficient mass transport with a reduced pressure drop through the flow channels in order to obtain a uniform current distribution and a high power density. In this investigation, three-dimensional fuel cell models are analyzed using computational fluid dynamics (CFD). The proposed flow fields are radially designed tree-shaped geometries that connect the center flow inlet to the perimeter of the fuel cell plate. Three flow geometries having different levels of bifurcation were investigated as flow channels for PEM fuel cells. The performance of the fuel cells is reported in polarization and power curves, and compared with that of fuel cells using conventional flow patterns such as serpentine and parallel channels. Results from the flow analysis indicate that tree-shaped flow patterns can provide a relatively low pressure drop as well as a uniform flow distribution. It was found that as the number of bifurcation levels increases, a larger active area can be utilized in order to generate higher power and current densities from the fuel cell with a negligible increase in pumping power.     

Reconstruction and optimization of catalyst layer of high temperature proton exchange membrane fuel cell

Catalyst layer determines the performance and cost of high temperature proton exchange membrane fuel cell. However, a fundamental understanding and optimization of its microstructure is hindered by the characterization limitation. In this study, the micro model and macro model are integrated to examine the effects of ionomer/carbon (I/C) ratio, Pt/C ratio and Pt loading on the microstructure and cell performance. As many Pt particles are partly coved by the ionomer, assuming all catalyst are covered by ionomer is inappropriate. Both Pt/C ratio and I/C ratio can significantly affect the compositions and microstructure of CL. The increase of Pt loading increases the cell performance, but the increment is small at a high platinum loading. However, the performance cost increases linearly with the increasing platinum loading. Thus, both cell performance and performance cost must be taken into consideration when designing CL of high temperature proton exchange membrane fuel cell.     

3D non-isothermal dynamic simulation of high temperature proton exchange membrane fuel cell in the start-up process

High temperature proton exchange membrane fuel cell (HT-PEMFC) with phosphoric acid doped polybenzimidazole (PBI) electrolyte shows multiple advantages over conventional PEMFC working at below 373 K, such as faster electrochemical kinetics, simpler water management, higher carbon monoxide tolerance. However, starting HT-PEMFC from room temperature to the optimal operating temperature range (433.15 K–453.15 K) is still a serious challenge. In present work, the start-up strategy is proposed and evaluated and a three-dimensional non-isothermal dynamic model is developed to investigate start-up time and temperature distribution during the start-up process. The HT-PEMFC is preheated by gas to 393.15 K, followed by discharging a current from the cell for electrochemical heat generation. Firstly, different current loads are applied when the average temperature of membrane reaches 393.15 K. Then, the start-up time and temperature distribution of co-flow and counter-flow are compared at different current loads. Finally, the effect of inlet velocity and temperature on the start-up process are explored in the case of counter-flow. Numerical results clearly show that applied current load is necessary to reduce start-up time and just 0.1 A/cm² current load can reduce startup time by 45%. It is also found that co-flow takes 18.8% less time than counter-flow to heat membrane temperature to 393.15 K, but the maximum temperature difference of membrane is 39% higher than the counter-flow. Increasing the inlet gas flow velocity and temperature can shorten the start-up time but increases the temperature difference of the membrane.     

Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization

The accurate mathematical model is an extremely useful tool for simulation and design analysis of fuel cell power systems. Particle swarm optimization (PSO) is a recently invented high-performance algorithm. In this work, a PSO-based parameter identification technique of proton exchange membrane (PEM) fuel cell models was proposed in terms of the voltage–current characteristics. Using the simulated and experimental voltage–current data, the validity of the proposed method has been confirmed. The results indicate that the PSO is an effective technique for identifying the parameters of PEM fuel cell models even in the presence of measuring noise. Moreover, the proposed method does not particularly necessitate initial guesses as close as possible to the solutions, required only is a broad range specified for each of the parameters. Therefore, the PSO method outperforms the GA and traditional optimization methods.     

Numerical study of high temperature proton exchange membrane fuel cell (HT-PEMFC) with a focus on rib design

The rib size is a critical engineering design parameter for high temperature proton exchange membrane fuel cell (HT-PEMFC) stack development, yet it hasn't been studied for HT-PEMFC. A three-dimensional, non-isothermal model was developed in this work to investigate the effect of channel to rib width ratios (CRWR) on the performance of HT-PEMFC. The reaction heat caused by entropy change was divided into cathodic half-reaction heat and anodic half-reaction heat. The results show that the ratio value significantly influence the gas diffusion, electron conduction and the distribution of current density in the porous electrodes. Increasing this ratio facilitates gas transport in the porous electrode but causes higher ohmic loss due to longer distance for electron conduction. As a result, an optimal ratio of about 1 is observed, which results in a peak power density of 0.428 W/cm². High current density is observed under the channel with a small ratio value while a high ratio value would cause high current density to appear under the rib, signifying the rib size effect on electrochemical behavior of HT-PEMFC. Apart from the electrical power output, the CRWR value also greatly influences the fluid flow and temperature distribution inside the cell, which would influence the long-term stability of HT-PEMFC. In the subsequent studies, efforts will be made to develop new stack configurations with more uniform gas distribution, short electron conduction path and low temperature gradient.     

Performance improvement of proton exchange membrane fuel cell by using annular shaped geometry

A complete three-dimensional and single phase CFD model for a different geometry of proton exchange membrane (PEM) fuel cell is used to investigate the effect of using different connections between bipolar plate and gas diffusion layer on the performances, current density and gas concentration. The proposed model is a full cell model, which includes all the parts of the PEM fuel cell, flow channels, gas diffusion electrodes, catalyst layers and the membrane. Coupled transport and electrochemical kinetics equations are solved in a single domain; therefore no interfacial boundary condition is required at the internal boundaries between cell components.This computational fluid dynamics code is used as the direct problem solver, which is used to simulate the three-dimensional mass, momentum and species transport phenomena as well as the electron- and proton-transfer process taking place in a PEMFC that cannot be investigated experimentally. The results show that the predicted polarization curves by using this model are in good agreement with the experimental results. Also the results show that by increasing the number of connection between GDL and bipolar plate the performance of the fuel cell enhances.     

Dynamic modeling of a high-temperature proton exchange membrane fuel cell with a fuel processor

A dynamic model of a high-temperature proton exchange membrane fuel cell with a fuel processor is developed in this study. In the model, a fuel processing system, a fuel cell stack, and an exhaust gas burner are modeled and integrated. The model can predict the characteristics of the overall system and each component at the steady and transient states. Specifically, a unit fuel cell model is discretized in a simplified quasi-three-dimensional geometry; therefore, the model can rapidly predict the distribution of fuel cell characteristics. Various operating conditions such as the steam-to-carbon ratio, oxygen-to-carbon ratio, and autothermal reforming inlet temperature are varied and investigated in this study. In addition, the dynamic characteristics exhibited during the transient state are investigated, and an efficiency controller is developed and implemented in the model to maintain the electrical efficiency. The simulation results demonstrate that the steam-to-carbon ratio and the oxygen-to-carbon ratio affect the electrical and system efficiency and that controlling the fuel flow rate maintains the electrical efficiency in the transient state. The model may be a useful tool for investigating the characteristics of the overall system as well as for developing optimal control strategies for enhancing the system performance.     

Experimentally and numerically investigating cell performance and localized characteristics for a high-temperature proton exchange membrane fuel cell

This paper is to experimentally and numerically investigate the cell performance and the localized characteristics associated with a high-temperature proton exchange membrane fuel cell (PEMFC). Three experiments are carried out in order to study the performance of the PEMFC with different operating conditions and to validate the numerical simulation model. The model proposed herein is a three-dimensional (3-D) computational fluid dynamics (CFD) non-isothermal model that essentially consists of thermal–hydraulic equations and electrochemical model. The performance curves of the PEMFC predicted by the present model agree with the experimental measured data. In addition, both the experiments and the predictions precisely demonstrate the enhanced effects of inlet gas temperature and system pressure on the PEMFC performance. Based on the simulation results, the localized characteristics within a PEMFC can be reasonably captured. These parameters include the fuel gas distribution, liquid water saturation distribution, membrane conductivity distribution, temperature variation, and current density distribution etc. As the PEMFC is operated at the higher current density, the fuel gas would be insufficiently supplied to the catalyst layer, consequently causing the decline in the generation of power density. This phenomenon is so called mass transfer limitation, which can be precisely simulated by the present CFD model.     

Study of cell voltage uniformity of proton exchange membrane fuel cell stack with an optimized artificial neural network model

The cell voltage uniformity of the proton exchange membrane fuel cell stack, which may consist of tens or hundreds of cells in series, plays a significant role in the stack's lifetime and performance. But it is challenging to predict the multi-cell voltages and the uniformity with a physics-based model due to complex stack geometry and huge computation efforts. In this work, we develop an artificial neural network model to estimate the steady-state cell voltage distributions of a 60 kW 140-cell stack. The optimized and well-trained model can efficiently reproduce the 140-cell voltages at different operating conditions with the error of less than 2 mV. The increased cathode gas pressure improves the cell voltage consistency and stack performance, while the voltage uniformity worsens with ascending load current. The efficient model prediction of cell voltages is beneficial for accurate evaluation of fuel cell performance, health state, and reliability.