ikumpli/LSTM-GANS-RUL-Prediction-for-Lithium-ion-Bateries
This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).
This project helps predict the remaining useful life (RUL) of lithium-ion batteries. By taking historical battery degradation data, it can forecast how much longer a battery will last before it needs replacement. This is useful for engineers, maintenance managers, or product developers working with battery-powered systems.
No commits in the last 6 months.
Use this if you need to accurately predict the end-of-life for lithium-ion batteries to optimize maintenance schedules or product lifecycles.
Not ideal if you are working with battery chemistries other than lithium-ion or require real-time RUL prediction without historical degradation data.
Stars
69
Forks
6
Language
Jupyter Notebook
License
—
Category
Last pushed
Apr 14, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ikumpli/LSTM-GANS-RUL-Prediction-for-Lithium-ion-Bateries"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
umbertogriffo/Predictive-Maintenance-using-LSTM
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in...
MichaelBosello/battery-rul-estimation
Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs
sileneer/NRP_2022_EEE12
LSTM and GRU model to predict the SOH of the batteries
uw-mad-dash/Battery-SoC-Estimation
Data and code for the paper 'Estimating Battery State-of-Charge within 1% using Machine Learning...
XiuzeZhou/RUL
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries