XiuzeZhou/RUL
A transformer-based deep learning model that predicts the remaining useful life of lithium-ion batteries using charge/discharge cycle data.
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This repository implements a transformer neural network architecture for predicting the remaining useful life (RUL) of lithium-ion batteries. The model processes time-series battery degradation data to estimate how many charge cycles remain before battery failure. It uses PyTorch as the deep learning framework and applies the transformer architecture (originally from NLP) to this regression task. The implementation includes support for NASA and CALCE battery datasets, with optional dropout and noise regularization parameters for improved robustness.
Frequently asked
- What is XiuzeZhou/RUL?
- A transformer-based deep learning model that predicts the remaining useful life of lithium-ion batteries using charge/discharge cycle data.
- Is RUL open source?
- Yes — XiuzeZhou/RUL is an open-source project tracked on heatdrop.
- What language is RUL written in?
- XiuzeZhou/RUL is primarily written in Jupyter Notebook.
- How popular is RUL?
- XiuzeZhou/RUL has 504 stars on GitHub.
- Where can I find RUL?
- XiuzeZhou/RUL is on GitHub at https://github.com/XiuzeZhou/RUL.