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Figures of NASA

Figures of CALCE

Due to the length of the paper, the two parameters ofdropout andnoise_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.

• pytorch 1.8.0

• pandas 0.24.2

• mixture_of_experts 0.2.1 (for AttMoE, github:https://github.com/lucidrains/mixture-of-experts)

• 6/5/2024, add figures of model and prediction
• 1/3/2024, upload the open sorce of AttMoE
• 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

1. 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/

2. NASA 锂电池数据集，基于 Python 的锂电池寿命预测:https://snailwish.com/395/

3. NASA 锂电池数据集，基于 python 的 MLP 锂电池寿命预测:https://snailwish.com/427/

4. NASA 和 CALCE 锂电池数据集，基于 Pytorch 的 RNN、LSTM、GRU 寿命预测:https://snailwish.com/497/

5. 基于 Pytorch 的 Transformer 锂电池寿命预测:https://snailwish.com/555/

6. 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/

@article{chen2022transformer,  title={Transformer network for remaining useful life prediction of lithium-ion batteries},  author={Chen, Daoquan and Hong, Weicong and Zhou, Xiuze},  journal={Ieee Access},  volume={10},  pages={19621--19628},  year={2022},  publisher={IEEE}}@article{chen2024attmoe,  title={AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries},  author={Chen, Daoquan and Zhou, Xiuze},  journal={Journal of Energy Storage},  volume={84},  pages={110780},  year={2024},  publisher={Elsevier}}