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README.md

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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 (v3)
+
+| Rank | Model | R² | MAE | Family |
+|------|-------|----|-----|--------|
+| 1 | XGBoost | 0.9866 | 1.58 | Classical |
+| 2 | GradientBoosting | 0.9860 | 1.38 | Classical |
+| 3 | LightGBM | 0.9826 | 1.98 | Classical |
+| 4 | RandomForest | 0.9814 | 1.83 | Classical |
+| 5 | ExtraTrees | 0.9701 | 3.20 | Classical |
+| 6 | TFT | 0.8751 | 3.88 | Transformer |
+| 7 | Weighted Avg Ensemble | 0.8991 | 3.51 | Ensemble |
+
+## 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}
+}
+```

v1/models.json

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+{
+  "version": "v1",
+  "display": "v1.0",
+  "description": "Classical ML models trained with cross-battery group split on 12 scalar features. Legacy baseline.",
+  "split_strategy": "cross-battery group split (80/20 batteries)",
+  "features": 12,
+  "feature_set": [
+    "cycle_number", "ambient_temperature", "peak_voltage", "min_voltage",
+    "voltage_range", "avg_current", "avg_temp", "temp_rise",
+    "cycle_duration", "Re", "Rct", "delta_capacity"
+  ],
+  "dataset": "NASA PCoE Li-ion 18650 (30 batteries, 2678 cycles)",
+  "default_model": "best_ensemble",
+  "models": {
+    "ridge": {
+      "display_name": "Ridge Regression",
+      "family": "classical",
+      "algorithm": "Ridge",
+      "version": "1.0",
+      "r2": 0.72,
+      "mae": 5.57,
+      "requires_scaling": true,
+      "file": "models/classical/ridge.joblib"
+    },
+    "lasso": {
+      "display_name": "Lasso",
+      "family": "classical",
+      "algorithm": "Lasso",
+      "version": "1.0",
+      "r2": 0.52,
+      "mae": 5.59,
+      "requires_scaling": true,
+      "file": "models/classical/lasso.joblib"
+    },
+    "elasticnet": {
+      "display_name": "ElasticNet",
+      "family": "classical",
+      "algorithm": "ElasticNet",
+      "version": "1.0",
+      "r2": 0.52,
+      "mae": 5.59,
+      "requires_scaling": true,
+      "file": "models/classical/elasticnet.joblib"
+    },
+    "knn_k5": {
+      "display_name": "KNN (k=5)",
+      "family": "classical",
+      "algorithm": "KNeighborsRegressor",
+      "version": "1.0",
+      "r2": 0.72,
+      "mae": 2.40,
+      "requires_scaling": true,
+      "file": "models/classical/knn_k5.joblib"
+    },
+    "knn_k10": {
+      "display_name": "KNN (k=10)",
+      "family": "classical",
+      "algorithm": "KNeighborsRegressor",
+      "version": "1.0",
+      "r2": 0.724,
+      "mae": 2.69,
+      "requires_scaling": true,
+      "file": "models/classical/knn_k10.joblib"
+    },
+    "knn_k20": {
+      "display_name": "KNN (k=20)",
+      "family": "classical",
+      "algorithm": "KNeighborsRegressor",
+      "version": "1.0",
+      "r2": 0.717,
+      "mae": 3.19,
+      "requires_scaling": true,
+      "file": "models/classical/knn_k20.joblib"
+    },
+    "svr": {
+      "display_name": "SVR (RBF)",
+      "family": "classical",
+      "algorithm": "SVR",
+      "version": "1.0",
+      "r2": 0.805,
+      "mae": 7.56,
+      "requires_scaling": true,
+      "file": "models/classical/svr.joblib"
+    },
+    "random_forest": {
+      "display_name": "Random Forest",
+      "family": "classical",
+      "algorithm": "RandomForestRegressor",
+      "version": "1.0",
+      "r2": 0.957,
+      "mae": 4.78,
+      "requires_scaling": false,
+      "file": "models/classical/random_forest.joblib"
+    },
+    "xgboost": {
+      "display_name": "XGBoost",
+      "family": "classical",
+      "algorithm": "XGBRegressor",
+      "version": "1.0",
+      "r2": 0.847,
+      "mae": 8.06,
+      "requires_scaling": false,
+      "file": "models/classical/xgboost.joblib"
+    },
+    "lightgbm": {
+      "display_name": "LightGBM",
+      "family": "classical",
+      "algorithm": "LGBMRegressor",
+      "version": "1.0",
+      "r2": 0.928,
+      "mae": 5.53,
+      "requires_scaling": false,
+      "file": "models/classical/lightgbm.joblib"
+    },
+    "best_ensemble": {
+      "display_name": "Best Ensemble (RF+XGB+LGB)",
+      "family": "ensemble",
+      "algorithm": "WeightedAverage",
+      "version": "1.0",
+      "r2": 0.886,
+      "mae": 3.89,
+      "requires_scaling": false,
+      "components": ["random_forest", "xgboost", "lightgbm"],
+      "file": null
+    }
+  },
+  "scalers": {
+    "linear_scaler": "scalers/linear_scaler.joblib",
+    "sequence_scaler": "scalers/sequence_scaler.joblib"
+  },
+  "champion": "random_forest",
+  "training_date": "2025-12-01",
+  "framework": ["scikit-learn", "xgboost", "lightgbm"]
+}

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