High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression

Abstract State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuat...

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Autores principales: Jiahao Ren, Junfei Cai, Jinjin Li
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/fb1468810a154f6aa8ac62e8237402a0
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