Awesome

Machine Learning-Based Lithium-Ion Battery Capacity Estimation</br>

<img src="https://kr.mathworks.com/matlabcentral/mlc-downloads/downloads/af72b217-588c-4847-ac0f-7f4d1561f32c/8eeee6c4-ff86-4fd3-a724-a326dae3493b/images/screenshot.png" width="90%"></img></br>

In this demo, using MATLAB, I've implemented machine learning based Lithium-Ion battery capacity estimation using multi-Channel charging Profiles.<br/>

Dataset used in this example is from "Battery data set" from NASA[1].<br/> 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. <br/> Throught this example, we will capture each approach described in paper, including following machine/deep learning methods<br/>

• FNN(Feed Forward Network)
• CNN(Convolutional Neural Network)
• LSTM(Long Short Term Memory Network)

Dataset should be downloaded from here: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#battery

[Reference]<br/> [1] B. Saha and K. Goebel, ``Battery data set,''NASA AMES Prognostics Data Repository, 2007.<br/> [2] Choi, Yohwan, et al. "Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles." IEEE Access 7 (2019): 75143-75152.<br/> Copyright 2019 The MathWorks, Inc.

[Cite as]</br> Wanbin Song (2019). Machine Learning Lithium-Ion Battery Capacity Estimation (https://www.github.com/wanbin-song/BatteryMachineLearning), GitHub. Retrieved November 26, 2019.