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

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.

Automated summary

This study presents a new optimized design of a hybrid AlexNet/Extreme Learning Machine (ELM) network for an optimal identification tool for Proton-exchange membrane fuel cells (PEMFCs). The proposed hybrid AlexNet/ELM is aimed at reducing the error between empirical and evaluated output voltages, and a modified version of the African Vulture Optimization (MAVO) Algorithm is suggested to enhance its model formation. The method is validated through a benchmark case study and compared with standard AlexNet/ELM, showing better confirmation with the experimental data.