Machine-learning techniques used to accurately predict battery life

Maitane Berecibar

Machine-learning techniques used to accurately predict battery life

Research output: Contribution to journal › Article › peer-review

Abstract

Highly reliable methods for predicting battery lives are needed to develop safe, long-lasting battery systems. Accurate predictive models have been developed using data collected from batteries early in their lifetime.

Original language	English
Pages (from-to)	325-326
Number of pages	2
Journal	Nature
Publication status	Published - Apr 2019

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title = "Machine-learning techniques used to accurately predict battery life",

abstract = "Highly reliable methods for predicting battery lives are needed to develop safe, long-lasting battery systems. Accurate predictive models have been developed using data collected from batteries early in their lifetime.",

author = "Maitane Berecibar",

year = "2019",

month = apr,

language = "English",

pages = "325--326",

journal = "Nature",

issn = "1476-4687",

publisher = "Nature Publishing Group",

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T1 - Machine-learning techniques used to accurately predict battery life

AU - Berecibar, Maitane

PY - 2019/4

Y1 - 2019/4

N2 - Highly reliable methods for predicting battery lives are needed to develop safe, long-lasting battery systems. Accurate predictive models have been developed using data collected from batteries early in their lifetime.

AB - Highly reliable methods for predicting battery lives are needed to develop safe, long-lasting battery systems. Accurate predictive models have been developed using data collected from batteries early in their lifetime.

M3 - Article

SP - 325

EP - 326

JO - Nature

JF - Nature

SN - 1476-4687

ER -

Machine-learning techniques used to accurately predict battery life

Abstract

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