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Research Papers

Dynamic Simulation of a Stationary Proton Exchange Membrane Fuel Cell System

[+] Author and Article Information
Kyoungdoug Min1

School of Mechanical and Aerospace Engineering, Seoul National University, Gwanakgu, Seoul 151-742, Republic of Koreakdmin@snu.ac.kr

Sanggyu Kang

School of Mechanical and Aerospace Engineering, Seoul National University, Gwanakgu, Seoul 151-742, Republic of Korea

Fabian Mueller, John Auckland, Jacob Brouwer

Mechanical and Aerospace Engineering, Department National Fuel Cell Research Center, University of California, Irvine, CA

1

Corresponding author.

J. Fuel Cell Sci. Technol 6(4), 041015 (Aug 17, 2009) (10 pages) doi:10.1115/1.3008029 History: Received July 11, 2007; Revised December 02, 2007; Published August 17, 2009

A dynamic model of a stationary proton exchange membrane (PEM) fuel cell system has been developed in MATLAB-SIMULINK ®. The system model accounts for the fuel processing system, PEM stack with coolant, humidifier with anode tail-gas oxidizer, and an enthalpy wheel for cathode air. Four reactors are modeled for the fuel processing system: (1) an autothermal reformation (ATR) reactor, (2) a high temperature shift (HTS) reactor, (3) a low temperature shift (LTS) reactor, and (4) a preferential oxidation reactor. Chemical kinetics for ATR that describe steam reformation of methane and partial oxidation of methane were simultaneously solved to accurately predict the reaction dynamics. The chemical equilibrium of CO with H2O was assumed at HTS and LTS reactor exits to calculate CO conversion corresponding to the temperature of each reactor. A quasi-one-dimensional PEM unit cell was modeled with five control volumes for solving the dynamic species and mass conservation equations and seven control volumes to solve the dynamic energy balance. The quasi-one-dimensional cell model is able to capture the details of membrane electrode assembly behavior, such as water transport, which is critical to accurately determine polarization losses. The dynamic conservation equations, primary heat transfer equations and equations of state are solved in each bulk component, and each component is linked together to represent the complete system. The model predictions well matched the observed experimental dynamic voltage, stack coolant outlet temperature, and catalytic partial oxidation (CPO) temperature responses to perturbations. The dynamic response characteristics of the current system are representative of a typical stationary PEM fuel cell system. The dynamic model is used to develop and test a proportional-integral (PI) fuel flow controller that determines the fuel flow rate to maintain the uniform system efficiency. The dynamic model is shown to be a useful tool for investigating the effects of inlet conditions, load, and fuel flow perturbations and for the development of control strategies for enhancing system performance.

Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic of the stationary fuel cell system model

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Figure 2

Schematic of the fuel processing system

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Figure 3

Control volumes for species conservation of PEM unit cell (not drawn to scale)

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Figure 4

Control volumes for energy conservation in the PEM unit cell (not drawn to scale)

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Figure 5

Comparison between experimental and simulated polarization curves

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Figure 6

dc current and fuel flow rate perturbations of Case 1

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Figure 7

Comparison of the experimental and simulated transient response of stack voltage and dc power to the Case 1 perturbation

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Figure 8

Comparison of the experimental and simulated stack efficiency and hydrogen utilization during the transient of Case 1

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Figure 9

Comparison between the experimental and simulated stack coolant outlet temperatures during the transient of Case 1

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Figure 10

Comparison of experimental and simulated transient CPO temperature response to the perturbation of Case 1

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Figure 11

dc current and fuel flow rate perturbations of Case 2

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Figure 12

Comparison of the experimental and simulated voltage and dc power response to the perturbation of Case 2

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Figure 13

Comparison of the experimental and simulated stack efficiencies and hydrogen utilization during the transient of Case 2

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Figure 14

Proposed system efficiency controller

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Figure 15

Comparison between the stack efficiency of the experiment (default) and simulation of the control model (control) used in Case 3

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Figure 16

Comparison between the fuel flow rate of the experiment (default) and that for simulation of control model (control) of Case 3

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Figure 17

Comparison of the transient experimental (default) stack efficiency and simulated (control) stack efficiency for the model of Case 4

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Figure 18

Comparison of the experimental (default) and simulated (control) fuel flow rate transients for Case 4

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