AI Battery Lifecycle — Model Repository

Trained model artifacts for the 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:

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

@misc{aiBatteryLifeCycle2025,
  author  = {Neeraj},
  title   = {AI Battery Lifecycle — SOH/RUL Prediction},
  year    = {2025},
  url     = {https://huggingface.co/spaces/NeerajCodz/aiBatteryLifeCycle}
}

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