Machine learning enhancements for electric vehicles: fault detection through deep Echo State Networks (ESN) model

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dc.contributor.authorBüyükbıçakçı, Erdal
dc.contributor.authorAtlı, Cahit
dc.contributor.authorDumanlı, Metin
dc.contributor.authorBulat, Selçuk
dc.contributor.authorNithiya, C.
dc.contributor.authorGulati, Monika
dc.date.accessioned2025-02-06T06:35:30Z
dc.date.available2025-02-06T06:35:30Z
dc.date.issued2024
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Sivil Havacılık Kabin Hizmetleri Programı
dc.description.abstractElectric vehicles (EVs) are generally considered to be more eco-friendly than traditional forms of mobility. Smart cities are environmentally conscious, and their operation is based on the conversion of electrical energy into mechanical energy using different kinds of motors. Motors in electric vehicles get electricity from renewable energy (RE) sources through power electronics-based interface connections in order to spin and provide mechanical power. The proposed approach incorporates preprocessing, feature extraction, and training of the model. Data cleansing, data noise reduction, data slicing, and integration are the primary components of data preparation. The principle of sparse principal component analysis (SPCA) is used for statistical analysis and analysis of multivariate datasets in order to identify features. The DESN model was utilized consistently throughout the training process. This state-of-the-art method outperforms SVM and LSTM with an average accuracy of 9 5. 6 5 %.
dc.identifier.doi10.1109/ICECA63461.2024.10801032
dc.identifier.endpage265
dc.identifier.isbn9798350367904
dc.identifier.scopus2-s2.0-85216195394
dc.identifier.scopusqualityN/A
dc.identifier.startpage260
dc.identifier.urihttps://doi.org/10.1109/ICECA63461.2024.10801032
dc.identifier.urihttps://hdl.handle.net/11501/1610
dc.indekslendigikaynakScopus
dc.institutionauthorAtlı, Cahit
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep Echo State Network (DESN)
dc.subjectElectric Vehicle Fault Detection
dc.subjectSparse Principal Component Analysis (SPCA)
dc.titleMachine learning enhancements for electric vehicles: fault detection through deep Echo State Networks (ESN) model
dc.typeConference Object

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