Reference
- D. Chen, W. Hong, and X. Zhou, "Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries", IEEE Access, 2022.
Supplement
Due to the length of the paper, the two parameters of dropout and noise_level are not discussed. By setting these two parameters, better results can be obtained than in the paper.
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noise level = 0.01: Setting the value of 1% disturbance is best: too large will degrade performance, too small will have little effect.
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dropout = 1e-4~1e-3: Set a small value for the network dropout to ensure the robustness of the model.
Packages
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pytorch 1.8.0
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pandas 0.24.2
Update
- 24, 2, 2022，Change some variable names
Dataset CALCE processing reference
https://github.com/konkon3249/BatteryLifePrediction
Please feel free to contact me: zhouxiuze@foxmail.com
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马里兰大学锂电池数据集 CALCE，基于 Python 的锂电池寿命预测: https://snailwish.com/437/
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