Optimizing PEMFC Performance through Advanced Control: A Machine Learning-Based MPPT Method

Optimizing PEMFC Performance through Advanced Control: A Machine Learning-Based MPPT Method | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 January 2025 V1 Latest version Share on Optimizing PEMFC Performance through Advanced Control: A Machine Learning-Based MPPT Method Author : Ayse Kocalmis Bilhan 0000-0002-5008-6784 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173676244.49743119/v1 193 views 100 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fuel cell (FC) systems offer promising solutions for the production of energy that is both efficient and environmentally friendly and are being developed for various applications such as residential, mobile, and vehicle. This advanced technology’s effectiveness, efficiency, and durability depend on a deep understanding, precise prediction, and effective management of the unique transient behaviors exhibited by the FC system. This study focuses on the development and analysis of a machine learning-based maximum power point tracking (ML-MPPT) controller for proton exchange membrane fuel cell (PEMFC) systems. The efficiency of the proposed controller was performed using MATLAB/Simulink, a powerful platform for dynamic system modeling and simulation. Firstly, a model was created using MATLAB/Simulink to evaluate the behavior of PEMFC. Subsequently, data collected from FC operating under diverse conditions were employed to train machine learning algorithms. Based on the simulation outcomes, it was observed that the proposed MPPT controller provides precise and fast MPPT performance. The simulation results demonstrate that the suggested MPPT controller achieves precise results in MPPT performance, exhibiting reduced power fluctuations and enhanced production efficiency. This emphasizes its potential to address the challenges encountered in PEMFC systems effectively. Supplementary Material File (akbilhan.docx) Download 756.33 KB Information & Authors Information Version history V1 Version 1 13 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords boost converter fuel cells machine learning mppt control pemfcs Authors Affiliations Ayse Kocalmis Bilhan 0000-0002-5008-6784 [email protected] Nevsehir Haci Bektas Veli Universitesi View all articles by this author Metrics & Citations Metrics Article Usage 193 views 100 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ayse Kocalmis Bilhan. Optimizing PEMFC Performance through Advanced Control: A Machine Learning-Based MPPT Method. Authorea . 13 January 2025. DOI: https://doi.org/10.22541/au.173676244.49743119/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.173676244.49743119/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00e1608da4dc13d',t:'MTc3OTY0NDQzMg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();