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Attention-Based Multimodal Transformer-LSTM Fusion Networks for Enhanced Lithium-ion Battery State-of-Charge Prediction with Aging Pattern Consideration

Crossref DOI link: https://doi.org/10.1007/s42835-026-02699-8

Published Online: 2026-02-26

Published Print: 2026-05

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

HyunKi, Ryu

Cho, SungRae

Jon, YongJun

Kim, Bonghwan

Kim, Dongkyun
Funding

Funding for this research was provided by:

Ministry of SMEs and Startups (RS-2022-00140464)
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Text and Data Mining valid from 2026-02-26

Version of Record valid from 2026-02-26
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Article History

Received: 22 September 2025

Revised: 10 February 2026

Accepted: 11 February 2026

First Online: 26 February 2026

Declarations

:

: No ethical approval was required for this study, as all data used are from publicly available sources and no human or animal subjects were involved.

: Not applicable.

: Not applicable.

: The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article.

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