Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC under Dynamic Load Conditions

Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC under Dynamic Load Conditions | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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 November 2025 V1 Latest version Share on Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC under Dynamic Load Conditions Authors : Rahul Singh 0000-0002-5986-8950 [email protected] , Mohit Kachhwaha , and Deepak Fulwani Authors Info & Affiliations https://doi.org/10.22541/au.176306056.68680266/v1 Published CES Transactions on Electrical Machines and Systems Version of record Peer review timeline 98 views 125 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Electric vehicle (EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance leads to significant torsional vibrations and speed fluctuations, which not only compromise passenger comfort but also exert additional mechanical stress on the EVs. Conventional sensorless methods offer speed estimation and control; however, they provide suboptimal performance with sudden load torque disturbances and operational uncertainties, especially at low speeds and across diverse real-world driving cycles. To address these challenges and improve system robustness, this work proposes an advanced sensorless integral sliding mode control (ASISMC) that enhances performance under diverse operating conditions. The proposed ASISMC methodology shows robust performance across a wide speed range, effectively mitigating abrupt load torque disturbances while minimizing the effect of uncertainties within the system dynamics. The approach is experimentally validated for a wide range of speeds and periodic/non-periodic load torque disturbances. Additional validation through the New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS) demonstrates the method's effectiveness and reliability in real-world driving conditions. Supplementary Material File (rahul_manuscript.pdf) Download 9.85 MB Information & Authors Information Version history V1 Version 1 13 November 2025 Peer review timeline Published CES Transactions on Electrical Machines and Systems Version of Record 1 Dec 2025 Published Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords asismc electric vehicles nedc robust control udds Authors Affiliations Rahul Singh 0000-0002-5986-8950 [email protected] View all articles by this author Mohit Kachhwaha View all articles by this author Deepak Fulwani View all articles by this author Metrics & Citations Metrics Article Usage 98 views 125 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rahul Singh, Mohit Kachhwaha, Deepak Fulwani. Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC under Dynamic Load Conditions. Authorea . 13 November 2025. DOI: https://doi.org/10.22541/au.176306056.68680266/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.176306056.68680266/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:'9ffd3c618d723fe2',t:'MTc3OTQ2Nzc0NQ=='};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())}}}})();