Reference
- D. Chen, W. Hong, and X. Zhou. "Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries", IEEE Access, 2022.
- D. Chen and X. Zhou. "AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries." Journal of Energy Storage 84 (2024): 110780.
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
-
mixture_of_experts 0.2.1 (for AttMoE)
Update
- 24, 2, 2022,Change some variable names
- 1, 3, 2024, upload the open sorce of AttMoE
Dataset CALCE processing reference
https://github.com/konkon3249/BatteryLifePrediction
Please feel free to contact me: [email protected]
更多内容
-
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/
-
NASA 锂电池数据集,基于 Python 的锂电池寿命预测: https://snailwish.com/395/
-
NASA 锂电池数据集,基于 python 的 MLP 锂电池寿命预测: https://snailwish.com/427/
-
NASA 和 CALCE 锂电池数据集,基于 Pytorch 的 RNN、LSTM、GRU 寿命预测: https://snailwish.com/497/
-
基于 Pytorch 的 Transformer 锂电池寿命预测: https://snailwish.com/555/
-
锂电池研究之七——基于 Pytorch 的高斯函数拟合时间序列数据: https://snailwish.com/576/