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Numerical and experimental state of identification battery pack lithium-ion

International Journal of Power Electronics and Drive Systems · Vol. 16 No. 4 (2025) · hlm. 2623–2633

DOI
10.11591/ijpeds.v16.i4.pp2623-2633
ISSN
2722-256X, 2088-8694
Terbit
2025-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Two key indicators of a battery management system (BMS) are the state of charge (SoC) and the state of health (SoH). Accurately estimating SoC is important to prevent potential issues. Additionally, space, computing time, and cost are important factors in hardware development. To address these considerations, the first-order extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) models were selected due to their simpler data pre-processing and better accuracy. The study recommends using the first-order equivalent circuit model (ECM) method in conjunction with the EKF and AEKF algorithms due to their straightforward setup and efficient computational process. Analysis of the charge-discharge cycles shows that the AEKF method consistently outperformed the EKF method regarding SoC accuracy. Moreover, when given different initial SoC values, the AEKF method displayed superior SoC estimation accuracy compared to the EKF method. Moreover, while the accuracy of the EKF is diminished, the error value remains below 2.5% for up to 500 cycles. Additionally, the shorter computing time of the EKF method is a consideration for practical real-world implementation. Furthermore, experiments conducted over 500 cycles revealed that SoH estimation declined from 99.97% to 76.1947%, suggesting that the battery has reached the end of life (EOL) stage.

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  • University of Indonesia
  • National Research and Innovation Agency
  • Telkom University
  • Nasional University
  • IPB University
  • Universitas Sumatera Utara
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Rekan penulisOrganisasiArtikel bersama
Imas Tri SetyadewiNational Research and Innovation Agency1
Yanuar PrabowoNational Research and Innovation Agency1
Priyo WibowoNational Research and Innovation Agency1
Rheo Loridho PratamaUniversity of Indonesia1
Ucuk DarusalamNasional University1
Muhammad ZarlisUniversitas Sumatera Utara1
Novi Dian NathasiaNasional University1
Rizki PermalaNational Research and Innovation Agency1
Hendra RahmawanIPB University1
Imas Sukaesih SitanggangIPB University1
M. Muaz Afra YunardiUniversity of Indonesia1
Erick Satriyo GumilangUniversity of Indonesia1
Chairul HudayaUniversity of Indonesia1
Faiz HusnayainUniversity of Indonesia1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Numerical and experimental state of identification battery pack lithium-ion</dc:title>
	<dc:creator>Anggraeni, Dewi</dc:creator>
	<dc:creator>Sudiarto, Budi</dc:creator>
	<dc:creator>Nasser, Eriko Nasemudin</dc:creator>
	<dc:creator>Hasbi, Wahyudi</dc:creator>
	<dc:creator>Natali, Yus</dc:creator>
	<dc:creator>Priambodo, Purnomo Sidi</dc:creator>
	<dc:subject xml:lang="en-US">AEKF; battery management system; EKF; lithium-ion; state of charge; state of health</dc:subject>
	<dc:description xml:lang="en-US">Two key indicators of a battery management system (BMS) are the state of charge (SoC) and the state of health (SoH). Accurately estimating SoC is important to prevent potential issues. Additionally, space, computing time, and cost are important factors in hardware development. To address these considerations, the first-order extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) models were selected due to their simpler data pre-processing and better accuracy. The study recommends using the first-order equivalent circuit model (ECM) method in conjunction with the EKF and AEKF algorithms due to their straightforward setup and efficient computational process. Analysis of the charge-discharge cycles shows that the AEKF method consistently outperformed the EKF method regarding SoC accuracy. Moreover, when given different initial SoC values, the AEKF method displayed superior SoC estimation accuracy compared to the EKF method. Moreover, while the accuracy of the EKF is diminished, the error value remains below 2.5% for up to 500 cycles. Additionally, the shorter computing time of the EKF method is a consideration for practical real-world implementation. Furthermore, experiments conducted over 500 cycles revealed that SoH estimation declined from 99.97% to 76.1947%, suggesting that the battery has reached the end of life (EOL) stage.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/24047</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v16.i4.pp2623-2633</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 16, No 4: December 2025; 2623-2633</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v16.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/24047/15104</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Dewi Anggraeni, Budi Sudiarto, Eriko Nasemudin Nasser, Wahyudi Hasbi, Yus Natali, Purnomo Sidi Priambodo</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>