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Comparison of Regression Methods for Estimation of State-of-Health in Lithium-Ion Batteries

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cris.author.scopus-author-id 56905465300
cris.author.scopus-author-id 58062879900
cris.author.scopus-author-id 58062879800
cris.author.scopus-author-id 57203803755
cris.author.scopus-author-id 38561331300
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department 97249ac5-6bf2-440d-b171-28ee875c9f9c
dc.contributor.author Denis Eka Cahyani
dc.contributor.author Faisal Farris Setyawan
dc.contributor.author Anjar Dwi Hariadi
dc.contributor.author Langlang Gumilar
dc.contributor.author Ahmad Kadri Junoh
dc.date.accessioned 2024-09-27T04:01:17Z
dc.date.available 2024-09-27T04:01:17Z
dc.date.issued 2023
dc.description.abstract Lithium-ion batteries are a type of rechargeable battery with a high energy density and an extended cycle life. The development of lithium-ion batteries is very rapid, but lithium-ion batteries have a limited lifespan and their energy storage capacity decreases with time and use. Therefore, the State of Health (SoH) of lithium-ion batteries is crucial when planning battery maintenance. The purpose of this study is to compare regression techniques for estimating the health of Li-ion batteries. XGBoost, Support Vector Regression (SVR), Random Forest Regression, Linear Regression, Gradient Boosting Regression, and Decision Tree Regression are the regression methods utilized in this investigation. All types of batteries from NASA's Prognostics Data Repository were utilized in the investigation. Support Vector Regression (SVR) yields the most accurate results compared to other techniques. The SVR technique yields RMSE, MSE, MAE, and MAPE values of 0.0226, 0.0005, 0.0208, and 0.0264, respectively. This indicates that the SVR method is capable of accurately estimating the SoH of a lithium-ion battery.
dc.identifier.doi 10.1109/IEIT59852.2023.10335524
dc.identifier.uri https://ieeexplore.ieee.org/document/10335524
dc.identifier.uri https://ieeexplore.ieee.org/Xplore/home.jsp
dc.language.iso en
dc.relation.funding Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Riau
dc.relation.grantno undefined
dc.relation.ispartof Proceedings - IEIT 2023: 2023 International Conference on Electrical and Information Technology
dc.subject Lithium-ion
dc.subject Regression
dc.subject State of Health (SoH)
dc.subject Support Vector Regression
dc.title Comparison of Regression Methods for Estimation of State-of-Health in Lithium-Ion Batteries
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.endPage 206
oaire.citation.startPage 202
oairecerif.affiliation.orgunit Universitas Negeri Malang
oairecerif.affiliation.orgunit Universitas Negeri Malang
oairecerif.affiliation.orgunit Universitas Negeri Malang
oairecerif.affiliation.orgunit Universitas Negeri Malang
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.author.affiliation Universitas Negeri Malang, Indonesia
oairecerif.author.affiliation Universitas Negeri Malang, Indonesia
oairecerif.author.affiliation Universitas Negeri Malang, Indonesia
oairecerif.author.affiliation Universitas Negeri Malang, Indonesia
oairecerif.author.affiliation Universiti Malaysia Perlis
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person.identifier.scopus-author-id 56905465300
person.identifier.scopus-author-id 58062879900
person.identifier.scopus-author-id 58062879800
person.identifier.scopus-author-id 57203803755
person.identifier.scopus-author-id 38561331300
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