This is a PyTorch implementation of the paper: Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning.
Ye Yuan, Guijun Ma, Songpei Xu
- System
- OS: Ubuntu 18.04
- GPU (one card):
- NVIDIA GeForce RTX 3090 (24 GB)
- CUDA: 11.1
- Driver: 470.57.02
- Python version
python = 3.8.8
This model is implemented using Python3 with dependencies specified in requirements.txt
# Install pytorch, see the official website for details: https://pytorch.org/
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
# Install other dependencies
pip install -r requirements.txt
Data Download
Yuan, Ye; Ma, Guijun; Xu, Songpei (2022), “The Dataset for: Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning ”, Mendeley Data, V2, doi: 10.17632/nsc7hnsg4s.2
- tool.py : Early stopping function
- common.py : Including data preprocessing, model training and validation
- net.py : Model structure
- prepare_ne_data.py : Data preprocessing for Task B
- prepare_nmc_data.py : Data preprocessing for Task C
- 1-wx_inner.ipynb : The pipeline of the Task A
- 2-ne2wx.ipynb : The pipeline of the Task B
- 3-nmc2lfp.ipynb : The pipeline of the Task C
If you have any questions, please contact songpeix@hust.edu.cn