Machine Learning Lithium-Ion Battery Capacity Estimation

Versión 1.0.1.2 (763 KB) por Wanbin Song
Machine learning based Lithium-Ion battery capacity estimation using multi-Channel charging Profiles
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(2)
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Actualizado 7 ene 2020
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In this script, I've implemented machine learning based Lithium-Ion battery capacity estimation using multi-Channel charging Profiles. Dataset used in this example is from "Battery data set" from NASA[1].
Basic implementation theory and approach is referenced by the recent published paper[2], and they proposed Multi-Channel charging profiles based machine learning and deep learning model for capacity estimation. Through this example, I will capture each approach described in paper.
[1] B. Saha and K. Goebel (2007). "Battery Data Set", NASA Ames Prognostics Data Repository (https://www.nasa.gov/intelligent-systems-division), NASA Ames Research Center, Moffett Field, CA
[2] Choi, Yohwan, et al. "Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles." IEEE Access 7 (2019): 75143-75152.

Citar como

Wanbin Song (2024). Machine Learning Lithium-Ion Battery Capacity Estimation (https://github.com/wanbin-song/BatteryMachineLearning), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2019b
Compatible con cualquier versión desde R2019b
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Versión Publicado Notas de la versión
1.0.1.2

Updated broken link in the description.
1.0.1.1

Updated result image
1.0.1

Divide dataset into Train/Validation/Test set to avoid overfitting
1.0.0.1

Connected to GitHub
1.0.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.