<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1665-6423</journal-id>
<journal-title><![CDATA[Journal of applied research and technology]]></journal-title>
<abbrev-journal-title><![CDATA[J. appl. res. technol]]></abbrev-journal-title>
<issn>1665-6423</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-64232024000300351</article-id>
<article-id pub-id-type="doi">10.22201/icat.24486736e.2024.22.3.2453</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Real driving cycle based SoC and battery temperature prediction for electric vehicle using AI models]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nainika]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Balamurugan]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Febin Daya]]></surname>
<given-names><![CDATA[J. L.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Anantha Krishnan]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Vellore Institute of Technology School of Electrical Engineering ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>India</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Vellore Institute of Technology Electric Vehicles: Incubation, Testing and Research Center ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>India</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>22</volume>
<numero>3</numero>
<fpage>351</fpage>
<lpage>361</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-64232024000300351&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-64232024000300351&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-64232024000300351&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The increase in electric vehicles has surpassed expectations leading to the eventual replacement of traditional IC (internal combustion) engine vehicles. However, to achieve this, it is crucial to research and develop more efficient and reliable electric batteries to create a sustainable transportation system. The performance of the battery directly impacts the power and range of the vehicle making battery management research imperative. Accurate estimation of battery state of charge (SoC) and temperature is vital for the overall performance, drivability and safety of the vehicle. This paper proposes a comprehensive approach to create an AI-based model to estimate the battery SoC and temperature that matches the performance of conventional vehicles. Various regression models are used as prediction models and the results are presented. These insights offer valuable understandings of battery thermal behavior, aiding in the design of an effective battery management system.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Electric vehicle]]></kwd>
<kwd lng="en"><![CDATA[battery]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[SoC estimation]]></kwd>
<kwd lng="en"><![CDATA[Temperature]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cai]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Qin]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Dual Time-scale State-Coupled Co-estimation of SOC, SOH and RUL for Lithium-Ion Batteries]]></article-title>
<source><![CDATA[ArXi]]></source>
<year>2022</year>
<volume>2210</volume>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dai]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain]]></article-title>
<source><![CDATA[IEEE transactions on industrial electronics]]></source>
<year>2018</year>
<volume>66</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>7706-16</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Deepthi]]></surname>
<given-names><![CDATA[N. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Febin Daya]]></surname>
<given-names><![CDATA[J. L.]]></given-names>
</name>
</person-group>
<source><![CDATA[Genetic algorithm based speed control of electric vehicle with electronic differential]]></source>
<year>2016</year>
<volume>6</volume>
<conf-name><![CDATA[ 6thInternational Conference, SEMCCO 2015]]></conf-name>
<conf-date>December 18-19, 2015</conf-date>
<conf-loc>India </conf-loc>
<page-range>128-42</page-range><publisher-name><![CDATA[Springer International Publishing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Febin]]></surname>
<given-names><![CDATA[D. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Sanjeevikumar]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Blaabjerg]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Wheeler]]></surname>
<given-names><![CDATA[P. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Olorunfemi Ojo]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ertas]]></surname>
<given-names><![CDATA[A. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Analysis of wavelet controller for robustness in electronic differential of electric vehicles: An investigation and numerical developments]]></article-title>
<source><![CDATA[Electric Power Components and Systems]]></source>
<year>2016</year>
<volume>44</volume>
<numero>7</numero>
<issue>7</issue>
<page-range>763-73</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ghosh]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Possibilities and challenges for the inclusion of the electric vehicle (EV) to reduce the carbon footprint in the transport sector: A review]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2020</year>
<volume>13</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>2602</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Godbin]]></surname>
<given-names><![CDATA[A. B.]]></given-names>
</name>
<name>
<surname><![CDATA[Jasmine]]></surname>
<given-names><![CDATA[S. G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Screening of COVID-19 based on GLCM features from CT images using machine learning classifiers]]></article-title>
<source><![CDATA[SN Computer Science]]></source>
<year>2023</year>
<volume>4</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>133</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hannan]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Lipu]]></surname>
<given-names><![CDATA[M. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Hussain]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Mohamed]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations]]></article-title>
<source><![CDATA[Renewable and Sustainable Energy Reviews]]></source>
<year>2017</year>
<volume>78</volume>
<page-range>834-54</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jawahar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Prassanna]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ravi]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Anbarasi]]></surname>
<given-names><![CDATA[L. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Jasmine]]></surname>
<given-names><![CDATA[S. G.]]></given-names>
</name>
<name>
<surname><![CDATA[Manikandan]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Kannan]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier]]></article-title>
<source><![CDATA[Multimedia Tools and Applications]]></source>
<year>2022</year>
<volume>81</volume>
<numero>28</numero>
<issue>28</issue>
<page-range>40451-68</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Kang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Xie]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Online state of health estimation of lithium-ion batteries based on charging process and long short-term memory recurrent neural network]]></article-title>
<source><![CDATA[Batteries]]></source>
<year>2023</year>
<volume>9</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>94</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Song]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wei]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Jiao]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Improved multiple-model adaptive estimation method for integrated navigation with time-varying noise]]></article-title>
<source><![CDATA[Sensors]]></source>
<year>2022</year>
<volume>22</volume>
<numero>16</numero>
<issue>16</issue>
<page-range>5976</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tang]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Zou]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Yao]]></surname>
<given-names><![CDATA[K. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Wik]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Load-responsive model switching estimation for state of charge of lithium-ion batteries]]></article-title>
<source><![CDATA[Applied energy]]></source>
<year>2019</year>
<volume>238</volume>
<page-range>423-34</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tian]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Wen]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Zeng]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[State-of-health prediction of lithium-ion batteries based on cnn-bilstm-am]]></article-title>
<source><![CDATA[Batteries]]></source>
<year>2022</year>
<volume>8</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>155</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tom]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Febin D. J. L.]]></surname>
</name>
</person-group>
<source><![CDATA[Vector Control of PMSM Drive in Electric Vehicles Using SVM Regression Approach]]></source>
<year>2023</year>
<conf-name><![CDATA[ International Conference on Communication and Intelligent Systems]]></conf-name>
<conf-loc> </conf-loc>
<page-range>345-59</page-range><publisher-loc><![CDATA[Singapore ]]></publisher-loc>
<publisher-name><![CDATA[Springer Nature Singapore]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Tang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism]]></article-title>
<source><![CDATA[Energy]]></source>
<year>2022</year>
<volume>244</volume>
</nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Lai]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Yao]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Yuan]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Weng]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[State estimation models of lithium-ion batteries for battery management system: status, challenges, and future trends]]></article-title>
<source><![CDATA[Batteries]]></source>
<year>2023</year>
<volume>9</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>131</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
