Machine Learning Lithium-Ion Battery Capacity Estimation

Machine learning based Lithium-Ion battery capacity estimation using multi-Channel charging Profiles

https://github.com/wanbin-song/BatteryMachineLearning

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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.

Cita come

Wanbin Song (2026). Machine Learning Lithium-Ion Battery Capacity Estimation (https://github.com/wanbin-song/BatteryMachineLearning), GitHub. Recuperato .

Informazioni generali

Compatibilità della release di MATLAB

  • Compatibile con R2019b e release successive

Compatibilità della piattaforma

  • Windows
  • macOS
  • Linux

Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate

Versione Pubblicato Note della release Action
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

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.
Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.