A modular Test Python framework for battery degradation.
✔ Battery voltage forecasting
✔ Remaining Useful Life (RUL)
✔ Missing-quarter detection
✔ Automatic interpolation
✔ ARIMA forecasting
✔ Adaptive Kalman filtering
✔ GRU forecasting
✔ LSTM forecasting
✔ Hybrid GRU-LSTM forecasting
✔ Hyperparameter optimization
✔ Forecast stability analysis
✔ Publication-quality visualization
Current Version
v0.1.0 (Development)
git clone https://github.com/<your-account>/BatteryHealthForecast.git
cd BatteryHealthForecast
pip install -e .from batteryhealthforecast import ForecastPipeline
pipe = ForecastPipeline()
pipe.load_csv("battery110.csv")
pipe.run()
pipe.show_dashboard()| Category | Models |
|---|---|
| Statistical | ARIMA |
| Probabilistic | Adaptive Kalman |
| Deep Learning | RNN, GRU, LSTM |
| Hybrid | GRU-LSTM |
| Future | Transformer, TCN |
- Repository foundation
- Data engine
- ARIMA
- Adaptive Kalman
- Deep Learning
- Hyperparameter Search
- RUL
- Dashboard
- Version 1.0
MIT License