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.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorAl-Dulaimi, Abdullah Ahmed
dc.contributor.authorGüneşer, Muhammet Tahir
dc.date.accessioned2025-04-18T08:21:35Z
dc.date.available2025-04-18T08:21:35Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
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.
dc.identifier.citationAl-Dulaimi, A. A., Guneser, M. T., & Hameed, A. A. (2024). A data-driven approach for diagnosing degradation in lithium-ion batteries using data transformation techniques and a novel deep neural network. Computers and Electrical Engineering, 117, 109313.
dc.identifier.doi10.1016/j.compeleceng.2024.109313
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85193823317
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.compeleceng.2024.109313
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6552
dc.identifier.volume117
dc.identifier.wosWOS:001246790400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherPergamon-elsevier science LTD
dc.relation.ispartofComputers & electrical engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLithium-İon Battery
dc.subjectBattery Health Diagnostics and Prognostics
dc.subjectDegradation Modes
dc.subjectDeep Learning
dc.subjectDeep Neural Networks
dc.titleA data-driven approach for diagnosing degradation in lithium-ion batteries using data transformation techniques and a novel deep neural network
dc.typeArticle

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