Open AccessAnalyze of Impedance for Water Management in Proton Exchange Membrane Fue Fells Using Neural Networks Methodology
Author(s) -
Slimane Laribi,
Khaled Mammar,
Fatima Zohra Arama,
Touhami Ghaitaoui
Publication year - 2019
Publication title -
algerian journal of renewable energy and sustainable development
Language(s) - English
Resource type - Journals
ISSN - 2710-849X
DOI - 10.46657/ajresd.2019.1.1.7
Subject(s) - resistor , electrical impedance , proton exchange membrane fuel cell , control theory (sociology) , sensitivity (control systems) , artificial neural network , stack (abstract data type) , oscillation (cell signaling) , nyquist plot , computer science , electronic engineering , voltage , engineering , electrode , control (management) , electrical engineering , dielectric spectroscopy , chemistry , fuel cells , artificial intelligence , chemical engineering , electrochemistry , programming language , biochemistry
The objective of this work is to define and to implement a simple method to assess the impacts of relative humidity and operating time on the fuel cell impedance. The method is based on the physical model of Randles with CPE and a mathematical tool for identifying various parameters based on the least squares’ method. The objective of the theoretical model development is the model implementation of the control system and water management of predictive diagnostics. Artificial neural networks are used to create the optimum impedance model. The model is applied for the identification of all resistors (internal resistors measured at high frequency, biasing resistors measured at high frequency) which are characterized by a high sensitivity for both cases, the flooding or drying of the cell heart (membrane and electrodes). This model is able to easily generate Nyquist diagram for any condition of relative humidity and operating time, it helped define the stack hydration status. Based on the obtained results, the model demonstrated a best flexible response, accurate and fast. The developed model can be integrated into a water management control system in PEM fuel cells.