Channel geometry optimization using a 2D fuel cell model and its verification for a polymer electrolyte membrane fuel cell

2014 ◽  
Vol 39 (17) ◽  
pp. 9430-9439 ◽  
Author(s):  
W.J. Yang ◽  
H.Y. Wang ◽  
Y.B. Kim
Author(s):  
P Rama ◽  
R Chen ◽  
R Thring

With the emerging realization that low temperature, low pressure polymer electrolyte membrane fuel cell (PEMFC) technologies can realistically serve for power-generation of any scale, the value of comprehensive simulation models becomes equally evident. Many models have been successfully developed over the last two decades. One of the fundamental limitations among these models is that up to only three constituent species have been considered in the dry pre-humidified anode and cathode inlet gases, namely oxygen and nitrogen for the cathode and hydrogen, carbon dioxide, and carbon monoxide for the anode. In order to extend the potential of theoretical study and to bring the simulation closer towards reality, in this research, a 1D steady-state, low temperature, isothermal, isobaric PEMFC model has been developed. The model accommodates multi-component diffusion in the porous electrodes and therefore offers the potential to further investigate the effects of contaminants such as carbon monoxide on cell performance. The simulated model polarizations agree well with published experimental data. It opens a wider scope to address the remaining limitations in the future with further developments.


Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 713
Author(s):  
Zhang Peng Du ◽  
Christoph Steindl ◽  
Stefan Jakubek

This paper proposes a new efficient two-step method for parametrizing control-oriented zero-dimensional physical polymer electrolyte membrane fuel cell (PEMFC) models with measured stack data. Parametrizations of these models are computationally intensive due to the numerous unknown parameters and the typically nonlinear, stiff model properties. This work reduces an existing model to decrease its stiffness for accelerated numerical simulations. Subdividing the parametrization into two consecutive subproblems (thermodynamic and electrochemical ones) reduces the solution space significantly. A parameter sensitivity analysis further reduces each sub-solution space by excluding non-significant parameters. The method results in an efficient parametrization process. The two-step approach minimizes each sub-solution space’s dimension by two-thirds, respectively three-fourths, compared to the global one. An achieved R2 value between simulation and measurement of 91% on average provides the required accuracy for control-oriented models.


Sign in / Sign up

Export Citation Format

Share Document