State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms.
- DOI
- 10.3390/s25154632
- Published
- 2025 Jul 26
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s25154632,
title = {State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms.},
author = {Carrera R and Quiroz L and Guevara C and Acosta-Vargas P},
year = {2025},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s25154632},
url = {https://doi.org/10.3390/s25154632}
}RIS
TY - JOUR TI - State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms. AU - Carrera R AU - Quiroz L AU - Guevara C AU - Acosta-Vargas P PY - 2025 JO - Sensors (Basel, Switzerland) DO - 10.3390/s25154632 UR - https://doi.org/10.3390/s25154632 ER -
APA
R, C., L, Q., C, G., & P, A. (2025). State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s25154632
Source records
- pubmed · retrieved 2026-11-10T07:13:34.793Z