Channel geometry optimization using a 2D fuel cell model and its verification for a polymer electrolyte membrane fuel cell

This paper studies the optimization method of channel geometries for a proton exchange membrane fuel cell (PEMFC) using a genetic algorithm (GA). The channel and rib widths and channel height are selected as geometry variables. The fuel cell output power is chosen as the cost function for the optimization. In this paper, an in-house genetic algorithm is constructed, and the fuel cell output power is obtained using an interfacing program connected to a commercial computational fluid dynamics (CFD) tool, COMSOL, in a Matlab environment. The 2D PEMFC is used to calculate the performance cost function for computational time and cost. The calculated output power of the PEMFC is delivered to the in-house GA program to check for optimality. After the optimality is checked, the new geometry data is fed back to the COMSOL to calculate the PEMFC output power until the optimization process is finished. Experiments are conducted to support the optimized results using three different channel geometries: channel-to-rib width ratios of 0.5:1, 1:1, and 2:1. A full 3D PEMFC CFD model is constructed using COMSOL to support the 2D CFD optimization results. This paper shows the possibility of applying the geometry optimization process to sophisticated electrochemical reaction systems, such as a PEMFC, using a GA and a commercial CFD tool on the Matlab platform. The geometries and materials can be optimized using this approach to obtain the most efficient performance of an electrochemical system.     

Real-time implementation of a neural model-based self-tuning PID strategy for oxygen stoichiometry control in PEM fuel cell

This paper proposes a real-time implementable self-tuning PID control strategy to tackle oxygen excess ratio regulation challenge of a proton exchange membrane fuel cell. Controller parameters are updated on-line, at each sampling time, using a not iterative procedure based on an artificial neural network model. The proposed controller takes account of nonlinear behaviors of the process, while avoiding heavy computations.     

Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

It is essential to develop an accurate model of proton exchange membrane fuel cell (PEMFC) for a reliable operation and analysis, in which unknown parameters usually need to be determined. The inherent nonlinear, strong coupling, and diversification of PEMFC model seriously hinder traditional methods to identify the parameters. For the sake of overcoming these thorny obstacles, Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification. Furthermore, the performance of LMBP is thoroughly evaluated and compared with four typical meta-heuristic algorithms under three cases. Simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification. In particular, accuracy and convergence speed can achieve as much as 99.8% and 95.9% growth via LMBP, respectively.     

Experimental analysis of dynamic characteristics on the PEM fuel cell stack by using Taguchi approach with neural networks

This study determines the optimum operating parameters for a proton exchange membrane fuel cell (PEMFC) stack to obtain small variation and maximum electric power output using a robust parameter design (RPD). The operating parameters examined experimentally are operating temperatures, operating pressures, anode/cathode humidification temperatures, and reactant flow rates. First, the dynamic Taguchi method is used to obtain the maximum and stable power density against the different current densities, which are regarded as the systemic inputs considered a signal factor. The relationship between control factors and responses in the PEMFC stack is determined using a neural network. The discrete parameter levels in the dynamic Taguchi method can be divided into desired levels to acquire real optimum operating parameters. Based on these investigations, the PEMFC stack is operated at the current densities of 0.4–0.8 A/cm². Since the voltage shift is quite small (roughly 0.73–0.83 V for each single cell), the efficiency would be higher. In the range of operation, the operating pressure, the cathode humidification temperature and the interactions between operating temperature and operating pressure significantly impact PEMFC stack performance. As the operating pressure increasing, the increments of the electric power decrease, and power stability is enhanced because the variation in responses is reduced.     

Convergence criteria establishment for 3D simulation of proton exchange membrane fuel cell

A validated 3 dimensional (3D) computational fluid dynamics model of a single cell proton exchange membrane fuel cell (PEMFC) was used for investigating convergence criteria. The simulation study was carried out using the commercial PEMFC simulation module built in to ANSYS FLUENT 12.1 software package and compared with published experimental data. Convergence data up to 19,000 iterations were collected in order to establish expectations for convergence errors and differences in convergence rates for different boundary conditions. Species mass fluxes and current density were used to perform a dual verification of experimentally verifiable simulation predictions. The results of the simulation showed that convergence trends were consistent for different boundary conditions and that the solution trends asymptotically to a final value with species mass flux errors approaching to constant values. The data were used to establish convergence criteria for future 3D PEMFC simulations where residual monitoring alone is insufficient to ensure convergence.     

Metamodel based design optimization approach in promoting the performance of proton exchange membrane fuel cells

This research presents a systematization and effectiveness approach in promoting the performance of the power density of a Proton Exchange Membrane Fuel Cell (PEMFC) by Metamodel-Based Design Optimization (MBDO). The proposed methodology of MBDO combines the design of experiment (DoE), metamodeling choice and global optimization. The fractional factorial experimental design method can screen important factors and the interaction effects in DoE, and obtain optimal design of the robust performance parameters by Taguchi method. Metamodeling then adopts the ability to establish a non-linear model of a complex PEMFC system configuration of an artificial neural network (ANN) based on the back-propagation network (BPN). Finally, on the many parameters (factors) of optimization, a genetic algorithm (GA) with a high capability for global optimization is used to search the best combination of the parameters to meet the requirement of the quality characteristics. Experimental results confirmed by the test equipment demonstrate that the MBDO approach is effective and systematic in promoting PEMFC performance of power density.     

Fast NMPC scheme of a 10 kW commercial PEMFC

This work highlights the gains of a fast nonlinear model-based predictive control (NMPC) scheme applied to a 10 kW proton exchange membrane fuel cell (PEMFC). The freshness of the approach is based on a particular parameterization of the control action to decrease the optimization problem dimension. Due to its short computational time, its reliability and its low sensitivity to noise, an artificial neural network (ANN) model is designed and used as a predictive model.     

Transient computation fluid dynamics modeling of a single proton exchange membrane fuel cell with serpentine channel

The proton exchange membrane fuel cell (PEMFC) has become a promising candidate for the power source of electrical vehicles because of its low pollution, low noise and especially fast startup and transient responses at low temperatures. A transient, three-dimensional, non-isothermal and single-phase mathematical model based on computation fluid dynamics has been developed to describe the transient process and the dynamic characteristics of a PEMFC with a serpentine fluid channel. The effects of water phase change and heat transfer, as well as electrochemical kinetics and multicomponent transport on the cell performance are taken into account simultaneously in this comprehensive model. The developed model was employed to simulate a single laboratory-scale PEMFC with an electrode area about 20 cm². The dynamic behavior of the characteristic parameters such as reactant concentration, pressure loss, temperature on the membrane surface of cathode side and current density during start-up process were computed and are discussed in detail. Furthermore, transient responses of the fuel cell characteristics during step changes and sinusoidal changes in the stoichiometric flow ratio of the cathode inlet stream, cathode inlet stream humidity and cell voltage are also studied and analyzed and interesting undershoot/overshoot behavior of some variables was found. It was also found that the startup and transient response time of a PEM fuel cell is of the order of a second, which is similar to the simulation results predicted by most models. The result is an important guide for the optimization of PEMFC designs and dynamic operation.     

Nature inspired flow field designs for proton exchange membrane fuel cell

Nature inspired flow field designs for proton exchange membrane fuel cells (PEMFCs) are a relatively recent development in the technology evolution. These novel designs have the potential to show dramatic performance improvements by effective distribution of reactant gases without water flooding. Optimization of a flow field requires balancing gas distribution, water management, electron transport, pressure drop and manufacturing simplicity. Computational fluid dynamics (CFD) simulation studies are a useful tool for evaluating nature inspired flow field designs; however, the predictions should be used with caution until validated by an experimental study. Nature inspired flow field designs can be generated using formal mathematical algorithms or by making heuristic modifications to existing natural structures. This paper reviews the current state of nature inspired PEMFC flow field designs and discusses the challenges in evaluating these designs.     

Development of a fast empirical design model for PEM stacks

A simple and fast empirical design model for a 5 kW proton exchange membrane (PEM) stack is presented in this paper. The performance analysis of the PEM stack operating on a membrane humidifying method is made through a series of experiments, including current–voltage–power characteristics, uniformity of cell unit voltages, gas pressure impact and air flux impact. Based on the above analysis, an empirical predicted model for the PEM stack has been developed by the combination of mechanistic and empirical modeling approaches to characterize and predict the voltage–current characteristics without examining in depth all physical/chemical phenomena. The good agreement between the predicted and experimental results covering a range of optimal operating conditions shows that the proposed model provides an accurate representation of the behavior for the PEM stack.     

基于BP神经网络的燃料电池电特性建模

采用基于改进粒子群算法的BP神经网（Improved—PSOBPNN）,建立质子交换膜燃料电池（PEMFC）电特性模型。PEMFC系统仿真结果表明该方法简单、有效、精度高,与采用传统BP神经网络的模型相比具有明显的优越性,为PEMFC系统建模,电池性能优化以及控制系统设计提供了新的思路。