http://journals.isel.pt/index.php/i-ETC/issue/feedi-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers2024-03-28T11:48:50+01:00i-ETC Editor: ISEL Academic Journal of ETCietc@deetc.isel.ipl.ptOpen Journal Systems<p>The “<em>ISEL Academic Journal of Electronics, Telecommunications and Computers</em>” (i-ETC) is a peer reviewed <strong>online open access</strong> journal maintained by the Department of Electronics, Telecommunications and Computer Engineering, at <a href="https://www.isel.pt/">ISEL: Instituto Superior de Engenharia de Lisboa.</a></p><p>The journal content is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/" rel="license">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p><p>The Journal features original contributions and review articles of theoretical and experimental research both at the fundamental and applied level in the broad fields of Electronics, Telecommunications and Computer Engineering (<a href="/index.php/i-ETC/about/editorialPolicies#focusAndScope">See details about topics here</a>)</p><p>The review process is pedagogically driven to improve quality of manuscripts submitted by young researchers, offering an easy access to a rigorous scientific peer reviewed process.</p><p>i-ETC charges no publication fees.</p><p><strong>i-ETC is:</strong></p><ul><li>Open Access</li><li>Peer Reviewed</li><li>Paperless</li><li>Young Researchers Oriented</li><li>With no Publication Fees</li></ul><p><a href="/index.php/i-ETC/about/submissions#authorGuidelines">Author guidelines here</a></p>http://journals.isel.pt/index.php/i-ETC/article/view/102An Approach to Estimate Electric Vehicle Driving Range2024-03-28T11:48:50+01:00David AlbuquerqueA43566@alunos.isel.ptArtur J Ferreiraartur.ferreira@isel.ptDavid P Coutinhodavid.coutinho@isel.ptThe use of electric vehicle (EV) has grown rapidly over the past few years. <br />The EV is now accepted as a reliable and eco-friendly means of transportation. <br />When choosing an EV, usually one of the key parameters of choice for the customer is its driving range (DR) capability. <br />This is a decisive factor since it minimizes the drivers anxiety on a trip. <br />The DR depends on many factors that must be taken into account when attempting its prediction.<br />In this paper, we explore the use of machine learning (ML) techniques to estimate the DR prediction.<br />We use regression techniques on models trained with publicly available datasets, evaluated with standard metrics.<br />The prediction results are better than those provided by statistical techniques, thus being quite encouraging.<br />As the end result, we also provide a ML benchmark written in Python, aiming to advance future research on this topic.2023-11-29T15:58:09+01:00Copyright (c) 2023 David Albuquerque