Using adaptive neuro-fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling
Journal of Mechanical Science and Technology
Department of Mechanical Engineering, Faculty of Engineering, Urmia University, Urmia, Iran; Department of Mechanical and Aeronautical Engineering, University of Pretoria Pretoria, Pretoria, South Africa; Department of Mechanical Engineering, Faculty of Engineering, Elm-o-Fan University, Urmia, Iran
In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used for modeling proton exchange membrane fuel cell (PEMFC) performance using some numerically investigated and compared with those to experimental results for training and test data. In this way, current density I (A/cm2) is modeled to the variation of pressure at the cathode side PC (atm), voltage V (V), membrane thickness (mm), Anode transfer coefficient αan, relative humidity of inlet fuel RHa and relative humidity of inlet air RHc which are defined as input (design) variables. Then, we divided these data into train and test sections to do modeling. We instructed ANFIS network by 80% of numerical-validated data. 20% of primary data which had been considered for testing the appropriateness of the models was entered ANFIS network models and results were compared by three statistical criterions. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states. © 2012 The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg.
Adaptive neuro-fuzzy inference system; ANFIS; Fuel cell performance; Inlet air; Membrane thickness; Network models; PEM fuel cell; Performance Modeling; Primary data; Statistical criterion; Test data; Test sections; Transfer coefficient; Atmospheric humidity; Fuzzy systems; Tracking (position); Proton exchange membrane fuel cells (PEMFC)