Solid Oxide Fuel Cell

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November 09, 2020, at 02:43 AM by 136.36.211.159 -
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Solid Oxide Fuel Cell

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January 08, 2016, at 01:01 AM by 10.5.113.189 -
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  1. Jacobsen, L. T. and Hedengren, J. D., Model Predictive Control with a Rigorous Model of a Solid Oxide Fuel Cell, American Control Conference (ACC), Washington, DC, 2013. Preprint
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  1. Jacobsen, L. T. and Hedengren, J. D., Model Predictive Control with a Rigorous Model of a Solid Oxide Fuel Cell, American Control Conference (ACC), Washington, DC, 2013. Preprint | Presentation
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July 02, 2013, at 08:17 PM by 10.5.113.195 -
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  1. Jacobsen, L. T. and Hedengren, J. D., Model Predictive Control with a Rigorous Model of a Solid Oxide Fuel Cell, American Control Conference (ACC), Washington, DC, 2013. Preprint
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  1. Jacobsen, L. T. and Hedengren, J. D., Model Predictive Control with a Rigorous Model of a Solid Oxide Fuel Cell, American Control Conference (ACC), Washington, DC, 2013. Preprint
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June 01, 2013, at 01:31 AM by 69.169.188.188 -
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Reference

B.J. Spivey and T.F. Edgar, Dynamic Modeling of Reliability Indicators in Solid Oxide Fuel Cells and Implications for Advanced Control, AIChE Fall 2010 Annual Meeting, Salt Lake City, Utah, November 2010.

Presentation (PDF)

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References

  1. B.J. Spivey and T.F. Edgar, Dynamic Modeling of Reliability Indicators in Solid Oxide Fuel Cells and Implications for Advanced Control, AIChE Fall 2010 Annual Meeting, Salt Lake City, Utah, November 2010. Presentation (PDF)
  2. Spivey, B.J., Hedengren, J.D., and Edgar, T.F., Constrained Control and Optimization of Tubular Solid Oxide Fuel Cells for Extending Cell Lifetime, American Control Conference (ACC), Montréal, Canada, July 2012. Preprint | Presentation
  3. Jacobsen, L. T. and Hedengren, J. D., Model Predictive Control with a Rigorous Model of a Solid Oxide Fuel Cell, American Control Conference (ACC), Washington, DC, 2013. Preprint
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March 13, 2012, at 05:15 AM by 69.169.188.228 -
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March 13, 2012, at 05:14 AM by 69.169.188.228 -
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December 02, 2011, at 12:43 AM by 10.5.113.210 -
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The distributed parameter approach produces a large number of states: 220 states for 10 finite volumes in the axial direction. The model is expressed as a collection of differential and algebraic equations that are solved simultaneously, without algebraic loops. With APMonitor modeling language, the algebraic equations are expressed in an implicit form. The model contains many nonlinearities which are introduced by reaction and electrochemical terms and temperature dependent properties.

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The distributed parameter approach produces a large number of states: 220 states for 10 finite volumes in the axial direction. The model is expressed as a collection of differential and algebraic equations that are solved simultaneously, without algebraic loops. The differential and algebraic equations are expressed in an implicit form. The model contains many nonlinearities which are introduced by reaction and electrochemical terms and temperature dependent properties.

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December 02, 2011, at 12:41 AM by 10.5.113.210 -
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