Citations refer to the original publication, not to a Scieee localized version.
Sylvestrin, G. R., Maciel, J. N., Amorim, M. L. M., Carmo, J. P., Afonso, J. A., Lopes, S. F., & Ando Junior, O. H. (2025). State of the art in electric batteries’ State-of-Health (SoH) estimation with machine learning: a review. MDPI. https://doi.org/10.3390/en18030746
Sylvestrin, Giovane Ronei, et al. State of the art in electric batteries’ State-of-Health (SoH) estimation with machine learning: a review. MDPI, 2025. https://doi.org/10.3390/en18030746.
Sylvestrin, Giovane Ronei, Joylan Nunes Maciel, Marcio Luís Munhoz Amorim, João Paulo Carmo, José A. Afonso, Sérgio F. Lopes, and Oswaldo Hideo Ando Junior. State of the art in electric batteries’ State-of-Health (SoH) estimation with machine learning: a review. MDPI, 2025. https://doi.org/10.3390/en18030746.
Sylvestrin, G. R. et al. (2025) State of the art in electric batteries’ State-of-Health (SoH) estimation with machine learning: a review. MDPI. Available at: https://doi.org/10.3390/en18030746.
G. R. Sylvestrin et al., “State of the art in electric batteries’ State-of-Health (SoH) estimation with machine learning: a review,” MDPI, 2025, doi: 10.3390/en18030746.
@misc{sylvestrin2025state,
author = {Sylvestrin, Giovane Ronei and Maciel, Joylan Nunes and Amorim, Marcio Luís Munhoz and Carmo, João Paulo and Afonso, José A. and Lopes, Sérgio F. and Ando Junior, Oswaldo Hideo},
title = {State of the art in electric batteries’ State-of-Health (SoH) estimation with machine learning: a review},
year = {2025},
publisher = {MDPI},
doi = {10.3390/en18030746},
url = {https://doi.org/10.3390/en18030746}
}
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