This paper presents Neural Network (NN) model of Polymer Electrolyte Membran (PEM) Fuel Cell for electric vehicle. The NN model simplifies the conventional model that considered thermodynamics, electrochemistry, hydrodynamics and mass transfer theory. The NN has a multilayer feed forward network structure and is trained using a back propagation learning rule. The NN model is used to predict the stack voltage of a PEM fuel cell to the vehicle speed. The data for the training of the NN model uses the parametric data that developed from the vehicle model and the PEM fuel cell model. The simulation results have shown that NN model can successfully predict the stack voltage to the vehicle speed. The performance of the network meets the requirement at epoch 50 and the error is 0.000000338.

Original languageEnglish
Pages (from-to)32-37
Number of pages6
JournalJournal of Theoretical and Applied Information Technology
Issue number1
Publication statusPublished - 2013


  • Back-propagation (BP)
  • Neural network
  • Proton exchange membrane fuel cell (PEMFC)
  • Vehicle model


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