A data-driven approach based on deep neural networks for lithium-ion battery prognostics

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dc.authoridKARA, Ahmet / 0000-0002-1590-0023
dc.contributor.authorKara, Ahmet
dc.date.accessioned2021-11-01T15:05:26Z
dc.date.available2021-11-01T15:05:26Z
dc.date.issued2021
dc.department[Belirlenecek]
dc.description.abstractRemaining useful life estimation is gaining attention in many real-world applications to alleviate maintenance expenses and increase system reliability and efficiency. Deep learning approaches have recently provided a significant improvement in the estimation of remaining useful life (RUL) and degradation progression concerning machinery prognostics. This research presents a new data-driven approach for RUL estimation using a hybrid deep neural network that combines CNN, LSTM, and classical neural networks. The presented CNN-LSTM neural network aims to extract the spatio-temporal relations in multivariate time series data and capture nonlinear characteristics to achieve better RUL prediction accuracy. To improve the proposed model's performance, PSO is handled to simultaneously optimize the hyperparameters of the network consisting of the number of epochs, the number of convolutional and LSTM layers, the size of units (or filters) in each convolutional, and LSTM layers. Besides, the proposed model in this paper, called the CNN-LSTM-PSO, realizes the multi-step-ahead prediction. In the experimental studies, the popular lithium-ion battery dataset presented by NASA is selected to verify the CNN-LSTM-PSO approach. The experimental consequences revealed that the presented CNN-LSTM-PSO model gives better results than other state-of-the-art machine learning techniques and deep learning approaches considering various performance criteria.
dc.identifier.doi10.1007/s00521-021-05976-x
dc.identifier.endpage13538en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue20en_US
dc.identifier.scopus2-s2.0-85105163120
dc.identifier.scopusqualityQ1
dc.identifier.startpage13525en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-05976-x
dc.identifier.urihttps://hdl.handle.net/11491/7271
dc.identifier.volume33en_US
dc.identifier.wosWOS:000642893800003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[Belirlenecek]
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectRemaining useful life predictionen_US
dc.subjectLong short-term memory (LSTM)en_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.subjectPrognosticsen_US
dc.subjectConvolutional neural networks (CNN)en_US
dc.titleA data-driven approach based on deep neural networks for lithium-ion battery prognostics
dc.typeArticle

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