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Research Papers

SOFC Stack Model for Integration Into a Hybrid System: Stack Response to Control Variables

[+] Author and Article Information
Michael M. Whiston

Department of Mechanical Engineering
and Materials Science,
University of Pittsburgh,
3700 O'Hara Street,
Pittsburgh, PA 15261
e-mail: mmw66@pitt.edu

Melissa M. Bilec

Associate Professor
Department of Civil
and Environmental Engineering,
University of Pittsburgh,
3700 O'Hara Street,
Pittsburgh, PA 15261
e-mail: mbilec@pitt.edu

Laura A. Schaefer

Professor
Department of Mechanical Engineering
and Materials Science,
University of Pittsburgh,
3700 O'Hara Street,
Pittsburgh, PA 15261
e-mail: las149@pitt.edu

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received November 11, 2014; final manuscript received December 18, 2014; published online March 10, 2015. Editor: Nigel M. Sammes.

J. Fuel Cell Sci. Technol 12(3), 031006 (Jun 01, 2015) (11 pages) Paper No: FC-14-1133; doi: 10.1115/1.4029877 History: Received November 11, 2014; Revised December 18, 2014; Online March 10, 2015

Due to the tight coupling of physical processes inside solid oxide fuel cells (SOFCs), efficient control of these devices depends largely on the proper pairing of controlled and manipulated variables. The present study investigates the uncontrolled, dynamic behavior of an SOFC stack that is intended for use in a hybrid SOFC-gas turbine (GT) system. A numerical fuel cell model is developed based on charge, species mass, energy, and momentum balances, and an equivalent circuit is used to combine the fuel cell's irreversibilities. The model is then verified on electrochemical, mass, and thermal timescales. The open-loop response of the average positive electrode-electrolyte-negative electrode (PEN) temperature, fuel utilization, and SOFC power to step changes in the inlet fuel flow rate, current density, and inlet air flow rate is simulated on different timescales. Results indicate that manipulating the current density is the quickest and most efficient way to change the SOFC power. Meanwhile, manipulating the fuel flow is found to be the most efficient way to change the fuel utilization. In future work, it is recommended that such control strategies be further analyzed and compared in the context of a complete SOFC-GT system model.

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References

Figures

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Fig. 1

SOFC model: (a) SOFC with channel highlighted, (b) channel with computational segment highlighted (CVs are indicated by dashed lines)

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Fig. 2

Representation of irreversible processes inside the SOFC: (a) equivalent circuit (adapted from Refs. [13,19,20,34]), (b) possible charge double layer in the SOFC (adapted from Ref. [20]), and (c) simplified equivalent circuit used to calculate the SOFC operating voltage (adapted from Refs. [19] and [33])

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Fig. 6

SOFC stack's response to a step change in the fuel flow rate: (a) millisecond timescale, (b) second timescale, and (c) minute timescale

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Fig. 5

Thermal voltage response. The dashed line indicates the estimated thermal voltage settling time based on Wang and Nehrir's results. The PEN temperature (axially averaged) is also shown.

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Fig. 4

Mass flow voltage response. The dashed line indicates the estimated mass flow voltage settling time based on Wang and Nehrir's results. The hydrogen mole fraction (axially averaged) is also shown.

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Fig. 9

SOFC stack's response to a step change in the air flow rate: (a) millisecond timescale, (b) second timescale, and (c) minute timescale

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Fig. 3

Electrochemical voltage response. The dashed line indicates the estimated electrochemical voltage settling time based on Wang and Nehrir's results. The double layer polarization (axially averaged) is shown for Cdbl = 10 mF.

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Fig. 7

SOFC stack's response to a step change in fuel flow rate assuming constant fuel utilization (Uf,2 = 85%)

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Fig. 8

SOFC stack's response to a step change in the current density: (a) millisecond timescale, (b) second timescale, and (c) minute timescale

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