Remaining capacity prediction of lithium battery based on BiLSTM-STW neural network

YAO Junrong; TANG Xueyong; LI Qingsheng

doi:10.3969/j.issn.1002-087X.2023.07.014

PDF(3111 KB)

Chinese Journal of Power Sources ›› 2023, Vol. 47 ›› Issue (7) : 889-893. DOI: 10.3969/j.issn.1002-087X.2023.07.014

Research and design: Chemical power sources

Remaining capacity prediction of lithium battery based on BiLSTM-STW neural network

{{article.zuoZhe_EN}}

Author information +

History +

HeighLight

Abstract

Key words

QR code of this article

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

{{article.zuoZheEn_L}}. {{article.title_en}}[J]. {{journal.qiKanMingCheng_EN}}, 2023, 47(7): 889-893 https://doi.org/10.3969/j.issn.1002-087X.2023.07.014

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

References

Funding

RIGHTS & PERMISSIONS

PDF(3111 KB)

Accesses

Citation

Detail

Sections

Recommended

Received	Published
2023-01-23	2023-07-20
Issue Date
2024-10-15

Please choose a citation manager

Content to export