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Fuel Cell Modeling In AMESim
IMAGINE Specific Thermodynamic Applications 04/2006 Cédric ROMAN –
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Introduction Fuel Cells are complex multi-domain dynamic systems
Electrical, electrochemical, fluidic, thermal phenomena are coupled Controlling such systems is a challenge to ensure efficiency and reliability Modelling fuel cells systems implies Interoperability Multi-disciplinary and dynamic simulation environment
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Power Based Fuel Cell Applications
k k k M M Portable electronics equipment Cars, boats, and domestic CHP Distributed power generation, CHP, also buses Higher energy density than batteries. Faster recharging Potential for zero emissions, higher efficiency Higher efficiency, less pollution, quiet Typical applications Power (W) Main advantages AFC MCFC Range of application of the different types of FC SOFC PEMFC PAFC
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Typical Fuel Cell PEM Control System
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Typical Fuel Cell PEM Control System
ELECTRICAL SUB-SYSTEM
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Typical Fuel Cell PEM Control System
ELECTRICAL SUB-SYSTEM
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Typical Fuel Cell PEM Control System
PNEUMATIC SYSTEM
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Typical Fuel Cell PEM Control System
PNEUMATIC SYSTEM
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Typical Fuel Cell PEM Control System
COOLING SYSTEM
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Typical Fuel Cell PEM Control System
COOLING SYSTEM
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Typical Fuel Cell PEM Control System
STACK SYSTEM
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Electric CFD Bond Graph Introduction
State of the art of PEMFC stack numerical models Dynamic model of analogic electrical equivalent system Pneumatics and chemicals are modelled with equivalent electric elements Quasi-steady state model based on CFD code Limited by boundary conditions CPU cost: days on parallelized clusters Bond-Graph model Multi-domain (electrical/chemical/pneumatic) Electric CFD Bond Graph
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AMESim Model for stack modelling
Stack System AMESim Model for stack modelling Inspired from Bond Graph Physical model of electrical, electrochemical, pneumatic and thermal phenomena Stack design and optimization Dynamic modelling of pneumatics, chemical reactions, etc…
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PEM cell Model structure (Explanations)
Cathode side Protons from anodic reaction H+ membrane Catalyst CL Porous media GDL gas mixture (O2,N2,H2O) width length/nel heigth x y Conductive flow rate O2 O2 O2 O2 O2 Diffusion Protonic resistance
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PEM cell Model structure (Explanations)
membrane CL H2O H2O H2O H2O H2O GDL e- e- e- e- e- Electrochemical reaction Nernst equation Reaction kinetic Current prediction & Ohmic losses Diffusion
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PEMFC Stack Model Core of model Interfaces electrochemical reaction
Electrical circuit Electrolyte Catalyst layer Electrical circuit Electrolyte (membrane) Catalyst layer Reaction parameters Stoechiometry in data file Reference heat of formation, standard entropy Kinetic parameters in data file Partial orders, kinetic constant Assymetry parameter
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PEMFC Stack model PEMFC cathode Electrochemical reaction
Gas mixture equilibrium potential Nernst equation Overpotential Activation Voltage – Equilibrium potential = Disequilibrium Reaction kinetic Butler-Volmer equation
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Gas mixture description
PEMFC Stack model Gas mixture description Dynamic description Mixture of N species Perfect gas equation of state Real gas possible Thermodynamic description JANAF 71: Cp, h, u, s given by 5 order polynomial of temperature Validity domain (200<->5000K) Diffusion Binary coefficients / Wilke formula Water condensation/vaporisation (to come…) Predefined species
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PEMFC Stack model Add-on Gas Mixture Basic Elements approach
Powerful features Initialisation facility Compatibility with PCD/PN/THPN
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Basic Elements approach
PEMFC Stack model Add-on Fuel Cells Basic Elements approach Compatible with Add-on Gas Mixture Thermal libraries
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Possible Discretizations
PEMFC Stack model Possible Discretizations Catalyst layer Gas diffusion layer Channel 7 nodes 13 nodes
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PEMFC Stack model Diffusion in porous media Ohmic losses
Double Capacitance layer Electrochemical reaction Diffusion in porous media Thermal Exchange Laminar flow in channel
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Serpentine configuration
PEMFC Stack model 80 Nodes Model Serpentine configuration
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PEMFC system simulation
AMESim Simulation Comparison of different architectures (different flowcharts) Design of the control of the PEMFC System Start-up process freeze start Change of load Drive cycle Power demand Power demand
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PEMFC AMESim model Allow quick results Robustness & Risk analysis
Physical model Transient behaviour Gas diffusion efficiency Thermal management Robustness & Risk analysis AMESim features Monte-Carlo simulation Design of experiment Optimization Sensitivity Analysis
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Gain Time & Performance Have a better understanding of physics
PEMFC AMESim model Gain Time & Performance Have a better understanding of physics Use all powerfuls AMESim applications Compatible with standard libraries Activity index Linear analysis (Bode, Nyquist, Nichols,…) Design of Experiment / Optimization Real-time
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