4,415 research outputs found

    Biomimetic flow fields for proton exchange membrane fuel cells: A review of design trends

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    Bipolar Plate design is one of the most active research fields in Polymer Electrolyte Membrane Fuel Cells (PEMFCs) development. Bipolar Plates are key components for ensuring an appropriate water management within the cell, preventing flooding and enhancing the cell operation at high current densities. This work presents a literature review covering bipolar plate designs based on nature or biological structures such as fractals, leaves or lungs. Biological inspiration comes from the fact that fluid distribution systems found in plants and animals such as leaves, blood vessels, or lungs perform their functions (mostly the same functions that are required for bipolar plates) with a remarkable efficiency, after millions of years of natural evolution. Such biomimetic designs have been explored to date with success, but it is generally acknowledged that biomimetic designs have not yet achieved their full potential. Many biomimetic designs have been derived using computer simulation tools, in particular Computational Fluid Dynamics (CFD) so that the use of CFD is included in the review. A detailed review including performance benchmarking, time line evolution, challenges and proposals, as well as manufacturing issues is discussed.Ministerio de Ciencia, Innovación y Universidades ENE2017-91159-EXPMinisterio de Economía y Competitividad UNSE15-CE296

    Modeling and simulations of deformation and transport in PEM fuel cells

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    Performance degradation and durability of PEM fuel cells depend strongly upon transport and deformation characteristics of their components especially the polymer membrane. Physical properties of the membrane, such as its ionic conductivity and Young’s modulus depend on its water content, which varies significantly with operating conditions and during transients. Recent studies indicate that cyclic transients may induce hygro-thermal fatigue that leads to the ultimate failure of the membrane shortening its lifetime, and thus, hindering the reliable use PEM fuel cells for automotive applications. In this work, we present two-dimensional simulations and analysis of coupled deformation and transport in PEM fuel cells. A two-dimensional cross-section of anode and cathode gas diffusion layers, and the membrane sandwiched between them is modeled using Maxwell-Stefan equations in gas diffusion layers, Biot’s poroelasticity and Darcy’s law for deformation and water transport in the membrane and Ohm’s law for ionic currents in the membrane and electric currents in the gas diffusion layers. Steady-state deformation and transport of water in the membrane, transient responses to step changes in load and relative humidity of the anode and cathode are obtained from simulation experiments, which are conducted by means of a commercial finite-element package, COMSOL Multiphysics

    Model-based analysis for the thermal management of open-cathode proton exchange membrane fuel cell systems concerning efficiency and stability

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    In this work we present a dynamic, control-oriented, concentrated parameter model of an open-cathode proton exchange membrane fuel cell system for the study of stability and efficiency improvement with respect to thermal management. The system model consists of two dynamic states which are the fuel cell temperature and the liquid water saturation in the cathode catalyst layer. The control action of the system is the inlet air velocity of the cathode air flow manifold, set by the cooling fan, and the system output is the stack voltage. From the model we derive the equilibrium points and eigenvalues within a set of operating conditions and subsequently discuss stability and the possibility of efficiency improvement. The model confirms the existence of a temperature-dependent maximum power in the moderate temperature region. The stability analysis shows that the maximum power line decomposes the phase plane in two parts, namely stable and unstable equilibrium points. The model is capable of predicting the temperature of a stable steady-state voltage maximum and the simulation results serve for the design of optimal thermal management strategies.Postprint (author's final draft

    Fuel Cell Modeling and Optimization

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    CFD Applications in Energy Engineering Research and Simulation: An Introduction to Published Reviews

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    Computational Fluid Dynamics (CFD) has been firmly established as a fundamental discipline to advancing research on energy engineering. The major progresses achieved during the last two decades both on software modelling capabilities and hardware computing power have resulted in considerable and widespread CFD interest among scientist and engineers. Numerical modelling and simulation developments are increasingly contributing to the current state of the art in many energy engineering aspects, such as power generation, combustion, wind energy, concentrated solar power, hydro power, gas and steam turbines, fuel cells, and many others. This review intends to provide an overview of the CFD applications in energy and thermal engineering, as a presentation and background for the Special Issue “CFD Applications in Energy Engineering Research and Simulation” published by Processes in 2020. A brief introduction to the most significant reviews that have been published on the particular topics is provided. The objective is to provide an overview of the CFD applications in energy and thermal engineering, highlighting the review papers published on the different topics, so that readers can refer to the different review papers for a thorough revision of the state of the art and contributions into the particular field of interest

    PEMFC performance improvement through oxygen starvation prevention, modeling, and diagnosis of hydrogen leakage

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    Catalyst degradation results in emerging pinholes in Proton Exchange Membrane Fuel Cells (PEMFCs) and subsequently hydrogen leakage. Oxygen starvation resulting from hydrogen leaks is one of the primary life-limiting factors in PEMFCs. Voltage reduces as a result of oxygen starvation, and the cell performance deteriorates. Starved PEMFCs also work as a hydrogen pump, increasing the amount of hydrogen on the cathode side, resulting in hydrogen emissions. Therefore, it is important to delay the occurrence of oxygen starvation within the Membrane Electrode Assembly (MEA) while simultaneously be able to diagnose the hydrogen crossover through the pinholes. In this work, first, we focus on catalyst configuration as a novel method to prevent oxygen starvation and catalyst degradation. It is hypothesized that the redistribution of the platinum catalyst can increase the maximum current density and prevent oxygen starvation and catalyst degradation. Therefore, a multi-objective optimization problem is defined to maximize fuel cell efficiency and to prevent oxygen starvation in the PEMFC. Results indicate that the maximum current density rises about eight percent, while the maximum PEMFC power density increases by twelve percent. In the next step, a previously developed pseudo two-dimensional model is used to simulate fuel cell behavior in the normal and the starvation mode. This model is developed further to capture the effect of the hydrogen pumping phenomenon and to measure the amount of hydrogen in the outlet of the cathode channel. The results obtained from the model are compared with the experimental data, and validation shows that the proposed model is fast and precise. Next, Machine Learning (ML) estimators are used to first detect whether there is a hydrogen crossover in the fuel cell and second to capture the amount of hydrogen cross over. K Nearest Neighbour (KNN) and Artificial Neural Network (ANN) estimators are chosen for leakage detection and classification. Eventually, a pair of ANN classifier-regressor is chosen to first isolate leaky PEMFCs and then quantify the amount of leakage. The classifier and regressor are both trained on the datasets that are generated by the pseudo two-dimensional model. Different performance indexes are evaluated to assure that the model is not underfitting/overfitting. This ML diagnosis algorithm can be employed as an onboard diagnosis system that can be used to detect and possibly prevent cell reversal failures
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