Toward Generalizable State-of-Charge Prediction of Lithium-Ion Batteries Using Deep Learning and Real-World Data
- DOI
- 10.3390/batteries12060185
- Published
- 2026-05-22
- Container
- Batteries
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/batteries12060185,
title = {Toward Generalizable State-of-Charge Prediction of Lithium-Ion Batteries Using Deep Learning and Real-World Data},
author = {Montaha Khedhiri and Rim Slama and Eduardo Redondo-Iglesias and Rochdi Trigui},
year = {2026},
journal = {Batteries},
doi = {10.3390/batteries12060185},
url = {https://doi.org/10.3390/batteries12060185}
}RIS
TY - JOUR TI - Toward Generalizable State-of-Charge Prediction of Lithium-Ion Batteries Using Deep Learning and Real-World Data AU - Montaha Khedhiri AU - Rim Slama AU - Eduardo Redondo-Iglesias AU - Rochdi Trigui PY - 2026 JO - Batteries DO - 10.3390/batteries12060185 UR - https://doi.org/10.3390/batteries12060185 ER -
APA
Khedhiri, M., Slama, R., Redondo-Iglesias, E., & Trigui, R. (2026). Toward Generalizable State-of-Charge Prediction of Lithium-Ion Batteries Using Deep Learning and Real-World Data. Batteries. https://doi.org/10.3390/batteries12060185
Source records
- crossref · retrieved 2026-09-25T00:13:27.160Z