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

Pugalenthi, Karkulali; Park, Hyunseok; Hussain, Shaista; Raghavan, Nagarajan

Pugalenthi, Karkulali; Park, Hyunseok; Hussain, Shaista; Raghavan, Nagarajan.

Afiliação

Pugalenthi K; Engineering Product Development Pillar, Singapore University of Technology & Design, 8 Somapah Road, Singapore 487372, Singapore.
Park H; Department of Information Systems, Hanyang University, Seoul 133791, Korea.
Hussain S; Computational Intelligence Group, A*STAR Institute of High-Performance Computing (IHPC), Singapore 138632, Singapore.
Raghavan N; Engineering Product Development Pillar, Singapore University of Technology & Design, 8 Somapah Road, Singapore 487372, Singapore.

Sensors (Basel) ; 22(10)2022 May 17.

Article em En | MEDLINE | ID: mdl-35632212

Assuntos

Lítio; Redes Neurais de Computação; Algoritmos; Teorema de Bayes; Fontes de Energia Elétrica; Íons

Palavras-chave

lithium-ion batteries; neural networks; particle filters; prognostics; remaining useful life

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Lítio Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google