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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)...
Autores principales: | Pugalenthi, Karkulali, Park, Hyunseok, Hussain, Shaista, Raghavan, Nagarajan |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
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 |
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