Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using s...
Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
About this item
Full title
Author / Creator
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements...
Alternative Titles
Full title
Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_37125291cee749d5afdfff954166db3d
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_37125291cee749d5afdfff954166db3d
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-98915-8
