| | --- |
| | license: mit |
| | language: |
| | - en |
| | tags: |
| | - battery |
| | - state-of-health |
| | - remaining-useful-life |
| | - time-series |
| | - regression |
| | - lstm |
| | - transformer |
| | - xgboost |
| | - lightgbm |
| | - random-forest |
| | - ensemble |
| | datasets: |
| | - NASA-PCoE-Battery |
| | metrics: |
| | - r2 |
| | - mae |
| | - rmse |
| | pipeline_tag: tabular-regression |
| | --- |
| | |
| | # AI Battery Lifecycle — Model Repository |
| |
|
| | Trained model artifacts for the [aiBatteryLifeCycle](https://huggingface.co/spaces/NeerajCodz/aiBatteryLifeCycle) project. |
| |
|
| | SOH (State-of-Health) and RUL (Remaining Useful Life) prediction for lithium-ion batteries |
| | trained on the NASA PCoE Battery Dataset. |
| |
|
| | ## Repository Layout |
| |
|
| | ``` |
| | artifacts/ |
| | ├── v1/ |
| | │ ├── models/ |
| | │ │ ├── classical/ # Ridge, Lasso, ElasticNet, KNN ×3, SVR, XGBoost, LightGBM, RF |
| | │ │ └── deep/ # Vanilla LSTM, Bi-LSTM, GRU, Attention-LSTM, TFT, |
| | │ │ # BatteryGPT, iTransformer, Physics-iTransformer, |
| | │ │ # DG-iTransformer, VAE-LSTM |
| | │ └── scalers/ # MinMax, Standard, Linear, Sequence scalers |
| | └── v2/ |
| | ├── models/ |
| | │ ├── classical/ # Same family + Extra Trees, Gradient Boosting, best_rul_model |
| | │ └── deep/ # Same deep models re-trained on v2 feature set |
| | ├── scalers/ # Per-model feature scalers |
| | └── results/ # Validation JSONs |
| | ``` |
| |
|
| | ## Model Performance Summary |
| |
|
| | | Rank | Model | R² | MAE | RMSE | Family | |
| | |------|-------|----|-----|------|--------| |
| | | 1 | Random Forest | 0.957 | 4.78 | 6.46 | Classical | |
| | | 2 | LightGBM | 0.928 | 5.53 | 8.33 | Classical | |
| | | 3 | Weighted Avg Ensemble | 0.886 | 3.89 | 6.47 | Ensemble | |
| | | 4 | TFT | 0.881 | 3.93 | 6.62 | Transformer | |
| | | 5 | Stacking Ensemble | 0.863 | 4.91 | 7.10 | Ensemble | |
| | | 6 | XGBoost | 0.847 | 8.06 | 12.14 | Classical | |
| | | 7 | SVR | 0.805 | 7.56 | 13.71 | Classical | |
| | | 8 | VAE-LSTM | 0.730 | 7.82 | 9.98 | Generative | |
| |
|
| | ## Usage |
| |
|
| | These artifacts are automatically downloaded by the Space on startup via |
| | `scripts/download_models.py`. You can also use them directly: |
| |
|
| | ```python |
| | from huggingface_hub import snapshot_download |
| | |
| | local = snapshot_download( |
| | repo_id="NeerajCodz/aiBatteryLifeCycle", |
| | repo_type="model", |
| | local_dir="artifacts", |
| | token="<your-token>", # only needed if private |
| | ) |
| | ``` |
| |
|
| | ## Framework |
| |
|
| | - **Classical models:** scikit-learn / XGBoost / LightGBM `.joblib` |
| | - **Deep models (PyTorch):** `.pt` state-dicts (CPU weights) |
| | - **Deep models (Keras):** `.keras` SavedModel format |
| | - **Scalers:** scikit-learn `.joblib` |
| |
|
| | ## Citation |
| |
|
| | ```bibtex |
| | @misc{aiBatteryLifeCycle2025, |
| | author = {Neeraj}, |
| | title = {AI Battery Lifecycle — SOH/RUL Prediction}, |
| | year = {2025}, |
| | url = {https://huggingface.co/spaces/NeerajCodz/aiBatteryLifeCycle} |
| | } |
| | ``` |
| |
|
