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Research Papers

Control-Oriented Modeling and Analysis of Air Management System for Fuel Reforming Fuel Cell Vehicle

[+] Author and Article Information
Nicolas Romani

Research Department, Renault, 1 Avenue du Golf, 78288 Guyancourt, France; Automatic Control Department,  Ecole Supérieure d’Electricité (Supélec), Plateau du Moulon, 91192 Gif-sur-Yvette, Francenicolas.romani@renault.com

Emmanuel Godoy

Automatic Control Department, Ecole Supérieure d’Electricité (Supélec), Plateau du Moulon, 91192 Gif-sur-Yvette, Franceemmanuel.godoy@supelec.fr

Dominique Beauvois

Automatic Control Department, Ecole Supérieure d’Electricité (Supélec), Plateau du Moulon, 91192 Gif-sur-Yvette, Francedominique.beauvois@supelec.fr

Vincent Le Lay

Research Department, Renault, 1 Avenue du Golf, 78288 Guyancourt, Francevincent.le-lay@renault.com

J. Fuel Cell Sci. Technol 5(1), 011009 (Jan 31, 2008) (13 pages) doi:10.1115/1.2784323 History: Received November 30, 2005; Revised June 12, 2006; Published January 31, 2008

With the purpose of meeting the specifically restrictive requirements of fuel reforming fuel cell vehicle, this paper brings into focus the issues of the transient operation of fuel cell systems and presents a control-oriented dynamic model of fuel cell air management system, suited for multivariable controller design, system optimization, and supervisory control strategy. In a first step, the dual analytical approach based on lumped and distributed parameter models is detailed: The partial differential equations deduced from mass/energy conservation laws and inertial dynamics are reduced to ordinary differential equations using spatial discretization and then combined with semiempirical actuator models to form the overall air system model. In a second step, a classical approach is followed to obtain a local linearization of the model. A validation of both nonlinear and linearized versions is performed by computational fluid dynamics simulations and experiments on a dedicated air system test bench. Thanks to dynamic analysis (pole/zero map), operating point impact and model order reduction are investigated. Finally, the multiinput multioutput state-space model—which balances model fidelity with model simplicity—can be coupled with reformer, stack, and thermal models to understand the system complexity and to develop model-based control methodologies.

Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 15

Influence of parameters on system eigenvalues

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Figure 16

Comparison of linear and nonlinear model outputs

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Figure 17

Structure of heat exchanger model

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Figure 18

Structure of FPS model

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Figure 19

Structure of FCS model

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Figure 20

The global FCPP model

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Figure 14

Influence of pressure and mass flow on system static gains

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Figure 1

Details on FCS structure and operation

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Figure 3

Scheme of the AMS

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Figure 4

Optimal FCPP operating profile

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Figure 5

The AMS test bench

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Figure 6

MOdel-based control design methodology.

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Figure 7

Bond-graph scheme of the general pipe model

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Figure 8

Example of pipe model validation

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Figure 9

Structure and variables of the global AMS model (two branches)

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Figure 10

Structure and variables of the reference model

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Figure 11

Linear compressor model and static flow map

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Figure 12

Direct and coupled system transfer functions

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Figure 13

Influence of pressure and mass flow on system eigenvalues

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