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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}
}
```
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