Research Papers

Application of Adaptive Neuro-Fuzzy Inference System Techniques to Predict Water Activity in Proton Exchange Membrane Fuel Cell

[+] Author and Article Information
Khaled Mammar

Department of Electrical
and Computer Engineering,
University of Béchar,
Bp 417,
Bechar 8000, Algeria
e-mail: mammar.univ.dz@gmail.com

Slimane Laribi

Unité de Recherche en Energies
Renouvelables en Milieu Saharien,
Centre de Développement des
Energies Renouvelables (CDER),
Adrar 01000, Algeria
e-mail: laribi_86@yahoo.fr

1Corresponding author.

Manuscript received November 12, 2017; final manuscript received April 18, 2018; published online May 9, 2018. Assoc. Editor: Matthew Mench.

J. Electrochem. En. Conv. Stor. 15(4), 041009 (May 09, 2018) (7 pages) Paper No: JEECS-17-1132; doi: 10.1115/1.4040058 History: Received November 12, 2017; Revised April 18, 2018

This work defines and implements a technique to predict water activity in proton exchange membrane fuel cell. This technique is based on the electrochemical impedance spectroscopy (EIS) as sensor and adaptive neuro-fuzzy inference system (ANFIS) as estimator. For this purpose, a proton exchange membrane fuel cell (PEMFC) model has been proposed to study the performances of the fuel cell for different operating conditions where the simulation model for water activity behavior is in the proposed structure. The technique based on ANFIS predicts the PEM fuel cell relative humidity (RH) from the EIS. For creation of ANFIS training and checking database, a new method based on factorial design of experimental is used. To check the proposed technique, the ANFIS estimator will be compared with the output humidity relative observation.

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Grahic Jump Location
Fig. 2

Polarization curve of the PEMFC with simultaneous changes water of RH and current density at stack temperature 90 deg

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Fig. 1

Basic operation of fuel cell and hydrated gas

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Fig. 5

Structure of an ANFIS

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Fig. 3

Power density curve of the PEMFC with simultaneous changes water of RH and current density at stack temperature 90 deg

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Fig. 8

Adaptive neuro-fuzzy inference system stricture and membership functions of input (input1 = Rhf, input2 = Rlf) for PEM fuel cell estimator

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Fig. 9

Adaptive neuro-fuzzy inference system estimator model surface

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Fig. 10

Simulation results

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Fig. 4

PEMFC impedance in Nyquist plot

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Fig. 7

Training and checking database for PEM fuel cell estimator ANFIS

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Fig. 11

PEMFC impedance behavior in Nyquist plot

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Fig. 6

Experimental impedance behavior manufactured by Fouquet et al. [14]



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