Contribution à l'étude du diagnostic du vieillissement d'une pile à combustible PEMFC

kahia, Hichem (2023) Contribution à l'étude du diagnostic du vieillissement d'une pile à combustible PEMFC. Doctoral thesis, SCIENCES ET DE LA TECHNOLOGIE.

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Abstract

In favor low emissions and high efficiency of fuel cell (FC), Fuel cell is regarded as next generation power devices in smart cities and sustainable mobility. Fuel cells convert the chemical energy stored in fuels to electricity in an electrochemically way. A suitable diagnostic is required to identify the different faults that may occur in fuel cell systems. The water management issue is particularly important in PEMFC. The role of the humidification circuit is to humidify the gases entering the fuel cell, generally from the water produced by the cell, recovered by means of a condenser. Drying or overwetting the membrane decreases electrical energy production and limits FC life. To ensure proper operation (yield, FC safety, response time, mechanical constraints, etc.), it is necessary to have a global control system that acts on understanding, detection, diagnosis and isolation of each of these failure modes in the PEMFC. This work focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation.This study demonstrated that past and present relative humidity correlates with the electrochemical impedance spectroscopy parameters (EIS ), and an ANN control model is effective in health estimation of PEMFC and diagnosing water management related problems that cause performance deterioration, durability. The presented methods in this study provides many advantages compared to other techniques that require a large number of database and instruments, and this justified by the analysis in term of fast accurate prognostic, quick to implement and low cost.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Proton exchange membrane fuel cells (PEMFC), Artificial neural network, State of health (SOH), Artificial intelligence (AI).
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculté des Sciences et de la technologie > Département de Génie Electrique
Depositing User: Mr. Mourad Kebiel
Date Deposited: 12 Jun 2024 09:26
Last Modified: 12 Jun 2024 09:26
URI: http://thesis.univ-biskra.dz/id/eprint/6483

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