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State of charge prediction for new and second-life lithium-ion batteries based on the random forest machine learning technique

International Journal of Power Electronics and Drive Systems · Vol. 17 No. 1 (2026) · pp. 487–501

DOI
10.11591/ijpeds.v17.i1.pp487-501
ISSN
2722-256X, 2088-8694
Published
2026-03-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Accurate state of charge (SOC) estimation is a critical requirement for the safe and efficient operation of lithium-ion batteries (LIBs), particularly in second-life battery (SLB) applications where battery ageing, nonlinear degradation, and measurement noise introduce uncertainty. Although numerous SOC estimation techniques have been proposed, reliable prediction for new and second-life batteries under varied operating conditions remains challenging. In this study, a comparative investigation of the conventional coulomb counting (CC) method and a data-driven random forest (RF) model is conducted for SOC prediction in new and second-life LIBs. Experimental data are obtained from Murata US18650VTC5D cells under pulse discharge tests (PDT), constant discharge tests (CDT), and dynamic stress tests (DST) across a wide range of C-rates. PDT is conducted at 0.24 C, CDT at 0.2 C, 0.5 C, 1 C, and 2 C, while DST is performed at C-rates ranging from 0.5 C to 4 C at a controlled ambient temperature of 25 °C. The RF model is trained using voltage, current, and time features and evaluated against CC using MAE, MSE, RMSE, and R² metrics. Results show that RF consistently outperforms CC under all conditions, particularly for SLBs, achieving significantly lower errors and R² values approaching 0.998. These findings confirm the effectiveness of RF-based SOC estimation for intelligent battery management systems (BMS).

Keywords

Collaboration network of the authors

40 authors · 111 co-authorship links · 35 across different organisations · showing the 40 most connected

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

  • University of Technology Malaysia
  • Universiti Teknikal Malaysia Melaka
  • Universitas Ahmad Dahlan
  • Islamic Azad University, Tehran
  • Diponegoro University
  • Universiti Malaysia Perlis
  • Universiti Sains Malaysia
  • 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
Auzani JidinUniversiti Teknikal Malaysia Melaka35
Mohammad JannatiUniversity of Technology Malaysia30
Mohd Hatta JopriUniversiti Teknikal Malaysia Melaka15
Awang JusohUniversity of Technology Malaysia15
Nik Rumzi Nik IdrisAffiliation unknown14
Mustafa ManapUniversiti Teknikal Malaysia Melaka10
Kasrul Abdul KarimAffiliation unknown9
Lina HandayaniUniversitas Ahmad Dahlan8
Mohamed SalemUniversiti Sains Malaysia8
Mahmoud ZadehbagheriIslamic Azad University, Tehran8
Abdul Rahim AbdullahUniversiti Teknikal Malaysia Melaka6
Mochammad FactaDiponegoro University6
Mohammad Javad KianiIslamic Azad University, Tehran5
Shahreen KasimAffiliation unknown5
Watra ArsadiandoAffiliation unknown5
Mohammad Faridun Naim TajuddinUniversiti Malaysia Perlis5
Mohd Junaidi Abdul AzizAffiliation unknown5
Shahrin Md AyobAffiliation unknown5
Imam Much Ibnu SubrotoSultan Agung Islamic University4
M. Rahimi YusoffUniversiti Teknikal Malaysia Melaka4
Muhammad AdoUniversity of Technology Malaysia4
Hendril Satrian PurnamaAffiliation unknown4
Anggit PamungkasAffiliation unknown4
Vahdat NazerianUniversity of Mazandaran4
Muhammad Zaid AihsanUniversiti Malaysia Perlis4
Chibuike Peter OhanuUniversity of Nigeria4
Tri WahonoAffiliation unknown4
Rahim IldarabadiAffiliation unknown4
Azralmukmin AzmiAffiliation unknown4
Siti Azura Ahmad TarusanAffiliation unknown4
Ibrahim AlhamrouniAffiliation unknown4
Aiman Zakwan JidinAffiliation unknown4
Munawar Agus RiyadiDiponegoro University3
Deris StiawanSriwijaya University3

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.

Metadata source: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">State of charge prediction for new and second-life lithium-ion batteries based on the random forest machine learning technique</dc:title>
	<dc:creator>Sahhouk, Masoud A.</dc:creator>
	<dc:creator>Aziz, Mohd Junaidi Abdul</dc:creator>
	<dc:creator>Ardani, Mohd Ibthisham</dc:creator>
	<dc:creator>Idris, Nik Rumzi Nik</dc:creator>
	<dc:creator>Sutikno, Tole</dc:creator>
	<dc:creator>Othman, Bashar Mohammad</dc:creator>
	<dc:subject xml:lang="en-US">lithium-ion battery; machine learning; new cells; second life cells; state of charge</dc:subject>
	<dc:description xml:lang="en-US">Accurate state of charge (SOC) estimation is a critical requirement for the safe and efficient operation of lithium-ion batteries (LIBs), particularly in second-life battery (SLB) applications where battery ageing, nonlinear degradation, and measurement noise introduce uncertainty. Although numerous SOC estimation techniques have been proposed, reliable prediction for new and second-life batteries under varied operating conditions remains challenging. In this study, a comparative investigation of the conventional coulomb counting (CC) method and a data-driven random forest (RF) model is conducted for SOC prediction in new and second-life LIBs. Experimental data are obtained from Murata US18650VTC5D cells under pulse discharge tests (PDT), constant discharge tests (CDT), and dynamic stress tests (DST) across a wide range of C-rates. PDT is conducted at 0.24 C, CDT at 0.2 C, 0.5 C, 1 C, and 2 C, while DST is performed at C-rates ranging from 0.5 C to 4 C at a controlled ambient temperature of 25 °C. The RF model is trained using voltage, current, and time features and evaluated against CC using MAE, MSE, RMSE, and R² metrics. Results show that RF consistently outperforms CC under all conditions, particularly for SLBs, achieving significantly lower errors and R² values approaching 0.998. These findings confirm the effectiveness of RF-based SOC estimation for intelligent battery management systems (BMS).</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>2026-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/24679</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v17.i1.pp487-501</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 17, No 1: March 2026; 487-501</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v17.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/24679/15171</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Masoud A. Sahhouk, Mohd Junaidi Abdul Aziz, Mohd Ibthisham Ardani, Nik Rumzi Nik Idris, Tole Sutikno, Bashar Mohammad Othman</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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