A data-driven approach for diagnosing degradation in lithium-ion batteries using data transformation techniques and a novel deep neural network

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dc.authoridGUNESER, Muhammet Tahir/0000-0003-3502-2034
dc.contributor.authorAl-Dulaimi, Abdullah Ahmed
dc.contributor.authorGuneser, Muhammet Tahir
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2024-09-29T15:55:14Z
dc.date.available2024-09-29T15:55:14Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractAccurate diagnosis of Lithium -ion batteries (Li -ion batteries) degradation plays a critical role in improving the maintenance of energy storage technology. This paper presents a method based on a novel deep network model combined with a data transformation technique to diagnose Li -ion battery degradation modes. Different from conventional studies based on specific experimental and numerical methods to estimate and predict the degradation, the proposed method is based on data -driven approach, by leveraging datasets consisting of voltage/capacity curves, these were converted into incremental capacity (IC) curves and then transformed into images using the gramian angular summation field (GASF) technique. The study adopted two models: Inception -v3 and the proposed model, both underwent fine-tuning and a subsequent transfer learning process. Degradation modes, namely loss of lithium inventory (LLI) and the loss of active materials in both the positive (LAMPE) and negative electrodes (LAMNE), were diagnosed in relation to IC curves. Finally, the model was tested using two different datasets, and the results showed that the proposed method achieved high performance, especially across three Li -ion batteries, three degradation modes, three cells, and various cycles (totaling 378 cases) the proposed method outperformed in 233 cases, thereby outperforming other methods in comparison. Our method provides a flexible data -driven approach that accurately predicts various degradation modes across different cell chemistries throughout their lifespan.en_US
dc.identifier.doi10.1016/j.compeleceng.2024.109313
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85193823317en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2024.109313
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4517
dc.identifier.volume117en_US
dc.identifier.wosWOS:001246790400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLithium -ion batteryen_US
dc.subjectBattery health diagnostics and prognosticsen_US
dc.subjectDegradation modesen_US
dc.subjectDeep learningen_US
dc.subjectDeep neural networksen_US
dc.titleA data-driven approach for diagnosing degradation in lithium-ion batteries using data transformation techniques and a novel deep neural networken_US
dc.typeArticleen_US

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