The Levenberg-Marquardt Neural Network model of the PEMFC´s MEA

Fuel cells are electrochemically complex, nonlinear, and dynamic energy conversion systems. Due to the dynamic characteristics of the fuel cell electrical performance models are used for system evaluation. In this study, Artificial Neural Network (ANN) technique is used as the modeling tool for internal structures of the fuel cells complex electrochemical reactions. The proton exchange membrane fuel cell (PEMFC) inputs are selected as anode flow, cathode flow, and cell temperature for the proposed Levenberg-Marquardt Neural Network model (LMNN). The outputs for the PEMFC model are current and voltage parameters. The model outputs are compared with the measured values and the maximum error is around 3%. The proposed ANN model is developed with MATLAB.