Yida Ge , Chu Zhang , Qianlong Liu , Xuedong Zhang , Jialei Chen , Muhammad Shahzad Nazir , Tian Peng
{"title":"A novel metaheuristic optimizer based on improved adaptive guided differential evolution algorithm for parameter identification of a PEMFC model","authors":"Yida Ge , Chu Zhang , Qianlong Liu , Xuedong Zhang , Jialei Chen , Muhammad Shahzad Nazir , Tian Peng","doi":"10.1016/j.fuel.2024.133869","DOIUrl":null,"url":null,"abstract":"<div><div>Proton exchange membrane fuel cells (PEMFCs) have the benefits of high efficiency, fast startup, and the ability to operate at low temperatures, which can improve the efficiency of energy utilization. Accurate parameter identification can enable the PEMFC model to better predict and simulate the performance of the system under dynamic operating conditions. Based on the semi-empirical model of PEMFC, an improved adaptive guided differential evolution (AGDE) algorithm is presented by adding the roulette wheel selection (RWS) optimization-based fitness-distance balance (RFDB) and Levy flight (LF) strategies, simplified as LRFDB-AGDE. The integration of multiple enhancement strategies is for a deeper optimization of the mutation mechanism architecture of the AGDE algorithm, aiming to enhance the local and global integrated search ability of the LRFDB-AGDE algorithm, which can identify the unknown parameters of the PEMFC model more efficiently and quickly. In this study, the superior parameter identification performance of the proposed LRFDB-AGDE algorithm is validated by simulating voltage and current data from four types of PEMFCs and comparing them with traditional intelligent algorithms such as AGDE and the whale optimization algorithm (WOA). Notably, the absolute errors of the LRFDB-AGDE algorithm in identifying the four PEMFCs are all within 5%.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"383 ","pages":"Article 133869"},"PeriodicalIF":6.7000,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fuel","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016236124030199","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Proton exchange membrane fuel cells (PEMFCs) have the benefits of high efficiency, fast startup, and the ability to operate at low temperatures, which can improve the efficiency of energy utilization. Accurate parameter identification can enable the PEMFC model to better predict and simulate the performance of the system under dynamic operating conditions. Based on the semi-empirical model of PEMFC, an improved adaptive guided differential evolution (AGDE) algorithm is presented by adding the roulette wheel selection (RWS) optimization-based fitness-distance balance (RFDB) and Levy flight (LF) strategies, simplified as LRFDB-AGDE. The integration of multiple enhancement strategies is for a deeper optimization of the mutation mechanism architecture of the AGDE algorithm, aiming to enhance the local and global integrated search ability of the LRFDB-AGDE algorithm, which can identify the unknown parameters of the PEMFC model more efficiently and quickly. In this study, the superior parameter identification performance of the proposed LRFDB-AGDE algorithm is validated by simulating voltage and current data from four types of PEMFCs and comparing them with traditional intelligent algorithms such as AGDE and the whale optimization algorithm (WOA). Notably, the absolute errors of the LRFDB-AGDE algorithm in identifying the four PEMFCs are all within 5%.
期刊介绍:
The exploration of energy sources remains a critical matter of study. For the past nine decades, fuel has consistently held the forefront in primary research efforts within the field of energy science. This area of investigation encompasses a wide range of subjects, with a particular emphasis on emerging concerns like environmental factors and pollution.