Health_status_prediction

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

Environment Setup

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

Requirements

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 Preparation

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

Code Introduction

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

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