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|Title:||Modeling of a PEM fuel-cell stack for dynamic and steady-state operation using ANN-based submodels|
|Keywords:||Artificial neural network (ANN)|
Proton exchange membrane (PEM)
|Citation:||Kong, X., Khambadkone, A.M. (2009-12). Modeling of a PEM fuel-cell stack for dynamic and steady-state operation using ANN-based submodels. IEEE Transactions on Industrial Electronics 56 (12) : 4903-4914. ScholarBank@NUS Repository. https://doi.org/10.1109/TIE.2009.2026768|
|Abstract:||A simple and accurate fuel-cell model is required for fuel-cell-based power-electronic applications. An artificial neural network (ANN) model is developed in this paper to model some nonlinear structures within the hybrid model of a protonexchange- membrane fuel-cell stack. It improves accuracy and allows the model to adapt itself to operating conditions. Moreover, the temperature effect on the fuel-cell stack is represented as the current effect by using ANN to help estimate the relationship between current and temperature. The real-time implementation of the proposed ANN model is realized via a dSPACE system. Experimental results are provided to verify the validity of the proposed model. © 2009 IEEE.|
|Source Title:||IEEE Transactions on Industrial Electronics|
|Appears in Collections:||Staff Publications|
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