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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) · hlm. 487–501

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

Abstrak

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).

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  • University of Technology Malaysia
  • Universiti Teknikal Malaysia Melaka
  • Universiti Malaysia Perlis
  • Universitas Ahmad Dahlan
  • Islamic Azad University Yasuj
  • Diponegoro University
  • Universiti Sains Malaysia
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Rekan penulisOrganisasiArtikel bersama
Auzani JidinUniversiti Teknikal Malaysia Melaka44
Mohammad JannatiUniversity of Technology Malaysia39
Awang JusohUniversity of Technology Malaysia19
Mohd Hatta JopriUniversiti Teknikal Malaysia Melaka18
Kasrul Abdul KarimUniversiti Teknikal Malaysia Melaka13
Mustafa ManapUniversiti Teknikal Malaysia Melaka12
Mohamed SalemUniversiti Sains Malaysia12
Norjulia Mohamad NordinUniversity of Technology Malaysia10
Abdul Rahim AbdullahUniversiti Teknikal Malaysia Melaka9
Shahrin Md AyobUniversity of Technology Malaysia9
Lina HandayaniUniversitas Ahmad Dahlan8
Shahreen binti KasimTun Hussein Onn University of Malaysia8
Mahmoud ZadehbagheriIslamic Azad University Yasuj8
Aiman Zakwan JidinUniversiti Teknikal Malaysia Melaka8
Siti Azura Ahmad TarusanUniversiti Teknikal Malaysia Melaka7
Nor Azizah Mohd YusoffUniversiti Teknikal Malaysia Melaka7
Ibrahim Mohd AlsofyaniUniversity of Technology Malaysia7
Mochammad FactaDiponegoro University6
Mohammad Faridun Naim TajuddinUniversiti Malaysia Perlis6
Ibrahim AlhamrouniUniversity of Kuala Lumpur6
M. Rahimi YusoffUniversiti Teknikal Malaysia Melaka5
Hendril Satrian PurnamaAfiliasi tidak diketahui5
Watra ArsadiandoAfiliasi tidak diketahui5
Azralmukmin AzmiUniversiti Malaysia Perlis5
Munawar Agus RiyadiDiponegoro University4
Imam Much Ibnu SubrotoSultan Agung Islamic University4
Muhammad AdoUniversity of Technology Malaysia4
Anggit PamungkasAfiliasi tidak diketahui4
Mohammad Javad KianiIslamic Azad University Yasuj4
Vahdat NazerianUniversity of Mazandaran4
Muhammad Zaid AihsanUniversiti Malaysia Perlis4
Chibuike Peter OhanuUniversity of Nigeria4
Tri WahonoAfiliasi tidak diketahui4
Rahim IldarabadiHakim Sabzevari University4

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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">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>
	<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/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>
</oai_dc:dc>