Anion exchange membrane fuel cell modelling

A parametric model predicting the performance of a solid polymer electrolyte, anion exchange membrane fuel cell (AEMFC), has been developed, in Matlab environment, based on interrelated electrical and thermal models. The electrical model proposed is developed by modelling an AEMFC open-circuit output voltage, irreversible voltage losses along with a mass balance, while the thermal model is based on the energy balance. The proposed model of the AEMFC stack estimates its dynamic behaviour, in particular the operating temperature variation for different discharge current values. The results of the theoretical fuel cell (FC) stack are reported and analysed in order to highlight the FC performance and how it varies by changing the values of some parameters such as temperature and pressure. Both the electrical and thermal FC models were validated by comparing the model results with experimental data and the results of other models found in the literature.     

Parameter optimization for a PEMFC model with a hybrid genetic algorithm

Many steady‐state models of polymer electrolyte membrane fuel cells (PEMFC) have been developed and published in recent years. However, models which are easy to be solved and feasible for engineering applications are few. Moreover, rarely the methods for parameter optimization of PEMFC stack models were discussed. In this paper, an electrochemical‐based fuel cell model suitable for engineering optimization is presented. Parameters of this PEMFC model are determined and optimized by means of a niche hybrid genetic algorithm (HGA) by using stack output‐voltage, stack demand current, anode pressure and cathode pressure as input–output data. This genetic algorithm is a modified method for global optimization. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder–Mead's simplex method into genetic algorithms (GAs). Calculation results of this PEMFC model with optimized parameters agreed with experimental data well and show that this model can be used for the study on the PEMFC steady‐state performance, is broader in applicability than the earlier steady‐state models. HGA is an effective and reliable technique for optimizing the model parameters of PEMFC stack. Copyright © 2005 John Wiley & Sons, Ltd.     

Modelling a PEM fuel cell stack with a nonlinear equivalent circuit

A nonlinear circuit model of a polymer electrolyte membrane (PEM) fuel cell stack is presented. The model allows the simulation of both steady-state and dynamic behaviour of the stack on condition that the values of some of its parameters are changed in the two operating conditions. The circuit parameters can be obtained by means of simple experimental tests and calculations. A commercial PEM fuel cell stack is modelled as seen from the power conditioning system side, without requiring parameters necessary for complex mathematical models and not easily obtainable by the majority of users. A procedure of parameter determination is developed and a comparison between the simulated and experimental results for both steady-state and dynamic behaviour of the PEM stack is shown.     

Dynamic fuel cell stack model for real-time simulation based on system identification

The authors have been developing an empirical mathematical model to predict the dynamic behaviour of a polymer electrolyte membrane fuel cell (PEMFC) stack. Today there is a great number of models, describing steady-state behaviour of fuel cells by estimating the equilibrium voltage for a certain set of operating parameters, but models capable of predicting the transient process between two steady-state points are rare. However, in automotive applications round about 80% of operating situations are dynamic. To improve the reliability of fuel cell systems by model-based control for real-time simulation dynamic fuel cell stack model is needed. Physical motivated models, described by differential equations, usually are complex and need a lot of computing time. To meet the real-time capability the focus is set on empirical models. Fuel cells are highly nonlinear systems, so often used auto-regressive (AR), output-error (OE) or Box-Jenkins (BJ) models do not accomplish satisfying accuracy. Best results are achieved by splitting the behaviour into a nonlinear static and a linear dynamic subsystem, a so-called Uryson-Model. For system identification and model validation load steps with different amplitudes are applied to the fuel cell stack at various operation points and the voltage response is recorded. The presented model is implemented in MATLAB environment and has a computing time of less than 1 ms per step on a standard desktop computer with a 2.8 MHz CPU and 504 MB RAM. Lab tests are carried out at DaimlerChrysler R&D Centre with DaimlerChrysler PEMFC hardware and a good agreement is found between model simulations and lab tests.     

Characterization of flooding and two-phase flow in polymer electrolyte membrane fuel cell stacks

A partially flooded gas diffusion layer (GDL) model is proposed and solved simultaneously with a stack flow network model to estimate the operating conditions under which water flooding could be initiated in a polymer electrolyte membrane (PEM) fuel cell stack. The models were applied to the cathode side of a stack, which is more sensitive to the inception of GDL flooding and/or flow channel two-phase flow. The model can predict the stack performance in terms of pressure, species concentrations, GDL flooding and quality distributions in the flow fields as well as the geometrical specifications of the PEM fuel cell stack. The simulation results have revealed that under certain operating conditions, the GDL is fully flooded and the quality is lower than one for parts of the stack flow fields. Effects of current density, operating pressure, and level of inlet humidity on flooding are investigated.     

Analysis of design parameters in anodic recirculation system based on ejector technology for PEM fuel cells: A new approach in designing

In this study, the anodic recirculation system (ARS) based on ejector technology in polymer electrolyte membrane PEM fuel cell is studied with employing a theoretical model. A practical method is presented for selecting or designing the ejector in an ARS, that offers the best selection or design. A comprehensive parametric study is performed on the design parameters of a PEM fuel cell stack and an ARS ejector. Four geometrical parameters consist of cell active area, cell number, nozzle throat diameter, and mixing chamber diameter in the design of ARS are intended. The effect of each contributes to the overall system performance parameters is studied. In this parametric study, the correlation between stack design parameters and ejector design parameters are studied. Eventually, based on the results, two dimensionless parameters are useful in the design process are proposed.     

Flow rate and humidification effects on a PEM fuel cell performance and operation

A new algorithm is presented to integrate component balances along polymer electrolyte membrane fuel cell (PEMFC) channels to obtain three-dimensional results from a detailed two-dimensional finite element model. The analysis studies the cell performance at various hydrogen flow rates, air flow rates and humidification levels. This analysis shows that hydrogen and air flow rates and their relative humidity are critical to current density, membrane dry-out, and electrode flooding. Uniform current densities along the channels are known to be critical for thermal management and fuel cell life. This approach, of integrating a detailed two-dimensional across-the-channel model, is a promising method for fuel cell design due to its low computational cost compared to three-dimensional computational fluid dynamics models, its applicability to a wide range of fuel cell designs, and its ease of extending to fuel cell stack models.     

A lumped parameter model of the polymer electrolyte fuel cell

A model of a polymer electrolyte fuel cell (PEFC) is developed that captures dynamic behaviour for control purposes. The model is mathematically simple, but accounts for the essential phenomena that define PEFC performance. In particular, performance depends principally on humidity, temperature and gas pressure in the fuel cell system. To simulate accurately PEFC operation, the effects of water transport, hydration in the membrane, temperature, and mass transport in the fuel cells system are simultaneously coupled in the model. The PEFC model address three physically distinctive fuel cell components, namely, the anode channel, the cathode channel, and the membrane electrode assembly (MEA). The laws of mass and energy conservation are applied to describe each physical component as a control volume. In addition, the MEA model includes a steady-state electrochemical model, which consists of membrane hydration and the stack voltage models.     

Analysis and control of a fuel delivery system considering a two-phase anode model of the polymer electrolyte membrane fuel cell stack

The fuel delivery system using both an ejector and a blower for a PEM fuel cell stack is introduced as a fuel efficiency configuration because of the possibility of hydrogen recirculation dependent upon load states.A high pressure difference between the cathode and anode could potentially damage the thin polymer electrolyte membrane. Therefore, the hydrogen pressure imposed to the stack should follow any change of the cathode pressure. In addition, stoichiometric ratio of the hydrogen should be maintained at a constant to prevent a fuel starvation at abrupt load changes.Furthermore, liquid water in the anode gas flow channels should be purged out in time to prevent flooding in the channels and other layers. The purging control also reduces the impurities concentration in cells to improve the cell performance.We developed a set of control oriented dynamic models that include a anode model considering the two-phase phenomenon and system components The model is used to design and optimize a state feedback controller along with an observer that controls the fuel pressure and stoichiometric ratio, whereby purging processes are also considered. Finally, included is static and dynamic analysis with respect to tracking and rejection performance of the proposed control.     

Innovative cathode flow-field design for passive air-cooled polymer electrolyte membrane (PEM) fuel cell stacks

A novel cathode flow-field design suitable for a passive air-cooled polymer electrolyte membrane (PEM) fuel cell stack is proposed to enhance the water-retaining capability under excess dry air supply conditions. The innovative cathode flow-field is designed to supply more air to the cooling channels and further enables deceleration of the reactant air in the gas channels and acceleration of the coolant air in the cooling channels simultaneously along the air flow path. Therefore, the design facilitates the waste heat removal through the cooling channels while the water removal by the reactant air is minimized. The conceptual cathode flow-field design is validated using a three-dimensional PEM fuel cell model. The detailed simulation results clearly demonstrate that the new cathode flow-field design exhibits superior water-retaining capability compared with a conventional cathode flow-field design (parallel flow channel configuration) under typical air-cooled fuel cell operating conditions. This study provides a new strategy to design cathode flow-fields to alleviate notorious membrane dehydration and unstable performance issues in a passive air-cooled PEM fuel cell stack.     

Online voltage consistency prediction of proton exchange membrane fuel cells using a machine learning method

Widely acknowledged by experts, the inconsistency between the cells of the proton exchange membrane fuel cell stack during operation is an important cause of the fuel cell life decay. Existing studies mainly focus on qualitative analysis of the effects of operating parameters on fuel cell stack consistency. However, there is currently almost no quantitative research on predicting the voltage consistency through operating parameters with machine learning methods. To solve this problem, a three-dimensional model of proton exchange membrane fuel cell stack with five single cells is established in this paper. The Computational Fluid Dynamic (CFD) method is used to provide the source data for prediction model. After predicting the voltage consistency with several machine learning methods and comparing the accuracy through simulation data, the integrated regression method based on Gradient Boosting Decision Tree (GBDT) gets the highest score (0.896) and is proposed for quickly predicting the consistency of cell voltage through operating parameters. After verifying the GBDT method with the experimental data from the fuel cell stack of SUNRISE POWER, in which the accuracy score is 0.910, the universality and accuracy of the method is confirmed. The influencing sensitivity of each operating parameter is evaluated and the current density has the greatest influence on the predicted value, which accounts for 0.40. The prediction of voltage consistency under different combination of operating parameters can guide the optimization of structural parameters in the process of the fuel cell design and operating parameters in the process of fuel cell control.     

Data driven models for a PEM fuel cell stack performance prediction

The operating principles of polymer electrolyte membrane (PEM) fuel cells system involve electrochemistry, thermodynamics and hydrodynamics theory for which it is not always easy to establish a mathematical model. In this paper two different methods to model a commercial PEM fuel cell stack are discussed and compared. The models presented are nonlinear, derived from a black-box approach based on a set of measurable exogenous inputs and are able to predict the output voltage and cathode temperature of a 5 kW module working at the CNR-ITAE. A PEM fuel cell stack fed with H₂ rich gas is employed to experimentally investigate the dynamic behaviour and to reveal the most influential factors. The performance obtained using a classical Neural Networks (NNs) model are compared with a number of stacking strategies. The results show that both strategies are capable of simulating the effects of different stoichiometric ratio in the output variables under different working conditions.     

Hierarchical fault diagnostic method for a polymer electrolyte fuel cell system

This study proposes a hierarchical method for on-line fault detection and diagnosis (FDD) of a stack and balance of plants (BoPs) in a polymer electrolyte fuel cell (PEFC) system. Because the fuel cell system consists of various subsystems with different characteristics, we have developed a multi-stage structure with subsystem-level FDD. In the first stage, faults were diagnosed at the subsystem level. In the next step, component-level faults were identified in the corresponding subsystem. The model-based approach in this study is composed of process estimation, residual generation, and FDD. Supervised machine learning methods were applied to train models for regression and fault classification. Residuals, the difference between analytic redundancies and measured results, were employed as fault indicators, i.e., residuals were used to detect faults and to generate fault patterns. Analytic redundancies were calculated using regression models. Several abrupt and performance degradation faults were considered. Because long-term performance degradations were difficult to introduce in the experimental system, the proposed method was evaluated using test data obtained by artificially decreasing the performance or sensor readings for a short period of time. This study focuses primarily on subsystem-level FDD and demonstrates one scenario of second level FDD. The experimental results verified the accuracy of the model-based approach and demonstrated that the proposed multi-stage hierarchical method effectively diagnosed faults in a PEFC system.     

Characterization and experimental results in PEM fuel cell electrical behaviour

A control oriented electrochemical static model of a proton exchange membrane fuel cell (PEMFC) stack is developed in this paper. Even though its validation is performed on a specific 7-cell PEMFC stack fed by humidified air and pure hydrogen, the methodology and fit parameters can be applied to different fuel cell systems with minor changes. The fuel cell model was developed combining theoretical considerations and semi-empirical analysis based on the experimental data. The proposed model can be successfully included into a larger dynamic subsystem to complete the power generation system.     

Analysis and control of a hybrid fuel delivery system for a polymer electrolyte membrane fuel cell

A polymer electrolyte membrane fuel cell (PEM FC) system as a power source used in mobile applications should be able to produce electric power continuously and dynamically to meet the demand of the driver by consuming the fuel, hydrogen. The hydrogen stored in the tank is supplied to the anode of the stack by a fuel delivery system (FDS) that is comprised of supply and recirculation lines controlled by different actuators. Design of such a system and its operation should take into account several aspects, particularly efficient fuel usage and safe operation of the stack.     

The experimental analysis of a dead-end H2/O2 PEM fuel cell stack with cascade type design

Proton exchange membrane fuel cells (PEMFCs) with a dead-ended anode and cathode can reach high hydrogen and oxygen utilization by a relatively simple system. Nevertheless, the accumulation of the water in the anode and cathode channels can lead to a local fuel starvation deteriorating the performance and the durability of PEMFCs. In this study, a novel design for a polymer electrolyte membrane (PEM) fuel-cell stack was presented which could achieve higher fuel utilization without using hydrogen and oxygen recirculation devices such as hydrogen pumps or ejectors that consume parasitic power and require additional control schemes. The basic concept of the innovatively proposed design was to divide the cells of a stack into several stages by conducting the outlet gas of each stage to a separator and reentering it into the next stage; thereby, a multistage anode and cathode system was prepared. In this relatively ingenious design, a higher gaseous flow rate was maintained at the cell outlet, even under dead-end conditions resulted in a reduced purge-gas emission by avoiding the accumulation of liquid water in the cells. The results revealed that proposed design had the same polarization curve as the open-end mode, leading to an enhanced PEMFC performance.     

Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine.     

Development of flow field design of polymer electrolyte membrane fuel cell using in-situ impedance spectroscopy

The design of a flow field channel of a polymer electrolyte membrane fuel cell (PEMFC) was investigated by computational fluid dynamic (CFD) simulation and in-situ three-channel impedance spectroscopy. To investigate the efficiency of the in-situ three-channel impedance spectroscopy method, it was adopted with a heterogeneous stack, which was composed according to three different types of flow field design. The in-situ three-channel method proved its validity by showing corresponding result with that obtained from the experiments and CFD simulation at the same experimental condition. This study demonstrates that a heterogeneous stack and in-situ three-channel impedance spectroscopy are powerful tools for predicting and analyzing the performance of a fuel cell stack.     

Cold start cycling durability of fuel cell stacks for commercial automotive applications

System durability is crucial for the successful commercialization of polymer electrolyte fuel cells (PEFCs) in fuel cell electric vehicles (FCEVs). Besides conventional electrochemical cycling durability during long-term operation, the effect of operation in cold climates must also be considered. Ice formation during start up in sub-zero conditions may result in damage to the electrocatalyst layer and the polymer electrolyte membrane (PEM). Here, we conduct accelerated cold start cycling tests on prototype fuel cell stacks intended for incorporation into commercial FCEVs. The effect of this on the stack performance is evaluated, the resulting mechanical damage is investigated, and degradation mechanisms are proposed. Overall, only a small voltage drop is observed after the durability tests, only minor damage occurs in the electrocatalyst layer, and no increase in gas crossover is observed. This indicates that these prototype fuel cell stacks successfully meet the cold start durability targets for automotive applications in FCEVs.