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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.

• noise level = 0.01: Setting the value of 1% disturbance is best: too large will degrade performance, too small will have little effect.

• dropout = 1e-4~1e-3: Set a small value for the network dropout to ensure the robustness of the model.

Packages

• pytorch 1.8.0

• pandas 0.24.2

Update

• 24, 2, 2022，Change some variable names

Dataset CALCE processing reference

https://github.com/konkon3249/BatteryLifePrediction

E-mail

Please feel free to contact me: [email protected]

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