Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning

With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predict the system/device’s remaining useful life (RUL)...

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Detalles Bibliográficos
Autores principales: Pugalenthi, Karkulali, Park, Hyunseok, Hussain, Shaista, Raghavan, Nagarajan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146325/
https://www.ncbi.nlm.nih.gov/pubmed/35632212
http://dx.doi.org/10.3390/s22103803

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146325/
https://www.ncbi.nlm.nih.gov/pubmed/35632212
http://dx.doi.org/10.3390/s22103803

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