Optimal model evaluation of the proton-exchange membrane fuel cells based on deep learning and modified African Vulture Optimization Algorithm

ABSTRACT

A new optimized design of a hybrid AlexNet/Extreme Learning Machine (ELM) network to provide an optimal identification tool for the Proton-exchange membrane fuel cells (PEMFCs) is presented in this study. The major concept is to reduce the error amount between the empirical output voltage and the evaluated output voltage of the PEM fuel cell stack model using the proposed hybrid AlexNet/ELM. For enhancing the model formation of the AlexNet/ELM, a modified version of the African Vulture Optimization (MAVO) Algorithm, which is a new metaheuristic, is suggested. To analyze the efficiency of the suggested method, it is applied to a practical PEMFC benchmark case study for identification purposes. Then, the method is confirmed by comparison of the experimental data and standard AlexNet/ELM. The achievements indicated the better confirmation of the suggested AlexNet/ELM network with the experimental data. The results show that the highest relative error for training and test is 0.03% and 0.05342%, respectively, which shows a promising result for the study.

KEYWORDS:

• PEM fuel cell
• parameter estimation
• AlexNet
• extreme learning machine
• output voltage
• Modified African Vulture Optimization Algorithm

Acknowledgments

(1) 2020 Guangzhou College of Technology and Business School-level Quality Engineering Construction Project “Big Data Course Teaching Reform Based on Chaoxing Fanya Network Teaching Platform – – ‘Data Analysis and Mining Practice (Python)’ as an Example” (Project Number: ZL20201243)

(2) 2021 Guangdong Provincial Department of Education Key Scientific Research Platform (Natural Science) for Colleges and Universities (Project Number:2021KTSCX350)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Nomenclature

Parameter	=	Definition
$A$	=	Membrane’s active area
$C_{O_2}$	=	Oxygen in the positive electrode catalytic interface
$C_{H_2}$	=	Hydrogen in the negative electrode catalytic interface
$E_{act}$	=	Activation voltage drop
$E_{cons}$	=	Concentration voltage drop
$E_{Nernst}$	=	Open circuit
$E_{\mathrm{\Omega }}$	=	Activation voltage drop
$I_{FC}$	=	Fuel cell current
$l$	=	Membrane thickness
$P_a$	=	input partial pressures for the positive electrodes
$P_c$	=	input partial pressures for the negative electrodes
$P_{H_2}$	=	Partial pressure of the hydrogen
$P_{O_2}$	=	Partial pressure of the oxygen
Rm	=	Represents the membrane resistance
$S$	=	Membrane surface
$T_F$	=	Cathode operational temperature
$T_{FC}$	=	PEMFC operating temperature
${\rho }_m$	=	Membrane resistivity
Rc	=	Connection resistance