Evaluating the Effectiveness of Boosting and Bagging Ensemble techniques in forecasting lithium-ion battery useful life

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dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 59417834100
dc.authorscopusidEdwin Geo Varuvel / 25225283500
dc.authorwosidFemilda Josephin Joseph Shobana Bai / JTQ-1812-2023
dc.authorwosidEdwin Geo Varuvel / AAE-5222-2022
dc.contributor.authorSonthalia, Ankit
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.contributor.authorAloui, Fethi
dc.contributor.authorVaruvel, Edwin Geo
dc.date.accessioned2025-04-18T06:45:54Z
dc.date.available2025-04-18T06:45:54Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractIt is essential to forecast the exact rate at which the cell's capacity would decline for practical uses, to comprehend the intricate and non-linear behavior of the cell. Furthermore, the majority of studies provided subpar prediction criteria, making early cell lifetime prediction difficult. Applying reliable and accurate aging models to the dynamic on-road conditions presents additional challenges. In this work, the battery lifetime during its earliest phases of use was accurately predicted using machine learning models. After analyzing the patterns of the parameters, 12 hand-crafted features were selected and the raw data of the first 100 cycles of 126 cells was used for creating the dataset for the features. The dataset was then used to train five machine learning models namely random forest, gradient boosting machine (GBM), light gradient boosting machine (LGBM), extreme gradient boosting machine (XGBoost), and gradient boost with categorical features (CATBoost). The statistical analysis reveals that XGBoost algorithm present the best result with a R2 value of 0.95 and root-mean-square-error (RMSE) of 97 cycles. Lastly, in comparison to existing studies, the RMSE significantly reduced from a maximum of 138 to 97 cycles.
dc.identifier.citationSonthalia, A., Josephin JS, F., Aloui, F., & Varuvel, E. G. (2025). Evaluating the Effectiveness of Boosting and Bagging Ensemble Techniques in Forecasting Lithium‐Ion Battery Useful Life. Energy Storage, 7(1), e70118.
dc.identifier.doi10.1002/est2.70118
dc.identifier.issn2578-4862
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85215327453
dc.identifier.scopusqualityQ3
dc.identifier.urihttp://dx.doi.org/10.1002/est2.70118
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6366
dc.identifier.volume7
dc.identifier.wosWOS:001394858500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBai, Femilda Josephin Joseph Shobana
dc.institutionauthorVaruvel, Edwin Geo
dc.institutionauthoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506
dc.institutionauthoridEdwin Geo Varuvel / 0000-0002-7303-3984
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofEnergy storage
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLife Time Prediction
dc.subjectLithium-Ion Battery
dc.subjectMachine Learning
dc.titleEvaluating the Effectiveness of Boosting and Bagging Ensemble techniques in forecasting lithium-ion battery useful life
dc.typeArticle

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