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Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 5 (2019)

DOI
10.11591/ijece.v9i5.pp3415-3422
ISSN
2722-2578, 2088-8708
Published
2019-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of the SoC.

Collaboration network of the authors

17 authors · 65 co-authorship links · 35 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Sidi Mohamed Ben Abdellah University
  • Université Moulay Ismail de Meknes
  • University of Hassan II Casablanca
  • University of Central Asia
  • Majmaah University
  • Siberian Federal University
  • Diplomatic Academy of Vienna
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mohamed AzeroualUniversité Moulay Ismail de Meknes6
Ayman AljarbouhUniversity of Central Asia3
Fatima Zahra BassineUniversity of Hassan II Casablanca3
Lahcen EliysaouyAffiliation unknown2
Aiman J.AlbarakatiMajmaah University2
Alexey KnyazkovSiberian Federal University2
Hassan El MoussaouiSidi Mohamed Ben Abdellah University2
Hassane El MarkhiSidi Mohamed Ben Abdellah University1
Soukaina NaciriSidi Mohamed Ben Abdellah University1
Ismail MoufidSidi Mohamed Ben Abdellah University1
Hassan El MoussaouiSidi Mohamed Ben Abdellah University1
Stephanie NessDiplomatic Academy of Vienna1
Lahcen ElyssaouiAffiliation unknown1
Mayur ReleParachute Health (United States)1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network</dc:title><dc:creator>Boujoudar, Younes</dc:creator><dc:creator>Elmoussaoui, Hassan</dc:creator><dc:creator>Lamhamdi, Tijani</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span class="fontstyle0"&amp;gt;In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of the&amp;lt;br /&amp;gt;SoC.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2019-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v9i5.pp3415-3422</dc:identifier><dc:identifier>10.11591/ijece.v9i5.pp3415-3422</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 9 No 5 (2019); 3415</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/15552/12932</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>