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A hybrid deep learning framework for joint state-of-charge and state-of-energy estimation of lithium-ion batteries under multiple operating conditions

Crossref DOI link: https://doi.org/10.1007/s11581-026-07076-z

Published Online: 2026-03-24

Published Print: 2026-05

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

Authors

Yu, Miao

Shi, Kaiming

Wang, Shunli

Zhang, Qin

Zhou, Lei

Dang, Quan

Fernandez, Carlos

Chen, Shaoqing
Funding

Funding for this research was provided by:

National Natural Science Foundation of Inner Mongolia Autonomous Region (2025ZD030)

National Natural Science Foundation of China (52377217, U23A20651)

National Key Research and Development Program (2024YFB2408400)

Sichuan Science and Technology Program (24NSFSC0024, 23ZDYF0734, 23NSFSC1436)

Dazhou City School Cooperation Project (DZXQHZ006)

Science and Technology Program of Administration for Market Retulation of Sichuan Province (SCSJS2023005)

Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines (SKLMRDPC23KF19)

Inner Mongolia Energy Strategy Research Center Project (24NYZL07)

Graduate Education Teaching Reform Project (24YJG027)
License Information

Text and Data Mining valid from 2026-03-24

Version of Record valid from 2026-03-24
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Article History

Received: 28 January 2026

Revised: 5 March 2026

Accepted: 12 March 2026

First Online: 24 March 2026

Declarations

:

: The authors declare no competing interests.

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