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Systematic Parameter Estimation and Sensitivity Analysis Using a Multidimensional PEMFC Model Coupled With DAKOTA

[+] Author Affiliations
Brian Carnes, Ken S. Chen

Sandia National Laboratories, Albuquerque, NM

Fangming Jiang, Gang Luo, Chao-Yang Wang

Pennsylvania State University, University Park, PA

Paper No. FuelCell2010-33038, pp. 455-461; 7 pages
doi:10.1115/FuelCell2010-33038
From:
  • ASME 2010 8th International Conference on Fuel Cell Science, Engineering and Technology
  • ASME 2010 8th International Fuel Cell Science, Engineering and Technology Conference: Volume 1
  • Brooklyn, New York, USA, June 14–16, 2010
  • Conference Sponsors: Advanced Energy Systems Division
  • ISBN: 978-0-7918-4404-5 | eISBN: 978-0-7918-3875-4
  • Copyright © 2010 by ASME

abstract

Current computational models for proton exchange membrane fuel cells (PEMFCs) include a large number of parameters such as boundary conditions, material properties, and numerous parameters used in sub-models for membrane transport, two-phase flow and electrochemistry. In order to successfully use a computational PEMFC model in design and optimization, it is important to identify critical parameters under a wide variety of operating conditions, such as relative humidity, current load, temperature, etc. Moreover, when experimental data is available in the form of polarization curves or local distribution of current and reactant/product species (e.g., O2, H2O concentrations), critical parameters can be estimated in order to enable the model to better fit the data. Sensitivity analysis and parameter estimation are typically performed using manual adjustment of parameters, which is also common in parameter studies. We present work to demonstrate a systematic approach based on using a widely available toolkit developed at Sandia called DAKOTA that supports many kinds of design studies, such as sensitivity analysis as well as optimization and uncertainty quantification. In the present work, we couple a multidimensional PEMFC model (which is being developed, tested and later validated in a joint effort by a team from Penn State Univ. and Sandia National Laboratories) with DAKOTA through the mapping of model parameters to system responses. Using this interface, we demonstrate the efficiency of performing simple parameter studies as well as identifying critical parameters using sensitivity analysis. Finally, we show examples of optimization and parameter estimation using the automated capability in DAKOTA.

Copyright © 2010 by ASME

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