Tabular Regression
Keras
Joblib
English
battery
state-of-health
remaining-useful-life
time-series
regression
lstm
transformer
xgboost
lightgbm
random-forest
ensemble
Instructions to use NeerajCodz/aiBatteryLifeCycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use NeerajCodz/aiBatteryLifeCycle with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://NeerajCodz/aiBatteryLifeCycle") - Notebooks
- Google Colab
- Kaggle
| { | |
| "version": "v3", | |
| "display": "v3.0", | |
| "description": "Production-grade models with cross-battery generalization split, 18 physics-informed features, and optimized hyperparameters. Highest accuracy across all versions.", | |
| "split_strategy": "cross-battery grouped split (no data leakage)", | |
| "features": 18, | |
| "feature_set": [ | |
| "cycle_number", | |
| "ambient_temperature", | |
| "peak_voltage", | |
| "min_voltage", | |
| "voltage_range", | |
| "avg_current", | |
| "avg_temp", | |
| "temp_rise", | |
| "cycle_duration", | |
| "Re", | |
| "Rct", | |
| "delta_capacity", | |
| "capacity_retention", | |
| "cumulative_energy", | |
| "dRe_dn", | |
| "dRct_dn", | |
| "soh_rolling_mean", | |
| "voltage_slope" | |
| ], | |
| "sequence_length": 32, | |
| "dataset": "NASA PCoE Li-ion 18650 (30 batteries, 2678 cycles)", | |
| "default_model": "best_ensemble", | |
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| "display_name": "XGBoost", | |
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| "display_name": "LightGBM", | |
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| "algorithm": "LGBMRegressor", | |
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| "display_name": "Random Forest", | |
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| "algorithm": "RandomForestRegressor", | |
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| "file": "models/classical/random_forest.joblib", | |
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| "svr": { | |
| "display_name": "SVR (RBF)", | |
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| "algorithm": "SVR", | |
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| "algorithm": "KNeighborsRegressor", | |
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| "bidirectional_lstm": { | |
| "display_name": "Bidirectional LSTM", | |
| "family": "deep_pytorch", | |
| "algorithm": "BidirectionalLSTM", | |
| "version": "3.0", | |
| "requires_scaling": true, | |
| "file": "models/deep/bidirectional_lstm.pt", | |
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| "gru": { | |
| "display_name": "GRU", | |
| "family": "deep_pytorch", | |
| "algorithm": "GRUModel", | |
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| "attention_lstm": { | |
| "display_name": "Attention LSTM", | |
| "family": "deep_pytorch", | |
| "algorithm": "AttentionLSTM", | |
| "version": "3.0", | |
| "requires_scaling": true, | |
| "file": "models/deep/attention_lstm.pt", | |
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| "batterygpt": { | |
| "display_name": "BatteryGPT", | |
| "family": "deep_pytorch", | |
| "algorithm": "BatteryGPT", | |
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| "family": "deep_pytorch", | |
| "algorithm": "TemporalFusionTransformer", | |
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| "physics_itransformer": { | |
| "display_name": "Physics iTransformer", | |
| "family": "deep_keras", | |
| "algorithm": "PhysicsITransformer", | |
| "version": "3.0", | |
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| "file": "models/deep/physics_itransformer.keras", | |
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| "display_name": "DG-iTransformer", | |
| "family": "deep_keras", | |
| "algorithm": "DynamicGraphITransformer", | |
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| "family": "deep_pytorch", | |
| "algorithm": "VAE_LSTM", | |
| "version": "3.0", | |
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| "display_name": "Stacking Ensemble", | |
| "family": "ensemble", | |
| "algorithm": "RidgeStacking", | |
| "version": "3.0", | |
| "requires_scaling": false, | |
| "file": "models/ensemble/ensemble_stacking.joblib", | |
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| ] | |
| }, | |
| "best_ensemble": { | |
| "display_name": "Best Ensemble (XGB+RF+ET+LSTM+TFT)", | |
| "family": "ensemble", | |
| "algorithm": "WeightedAverage", | |
| "version": "3.0", | |
| "requires_scaling": false, | |
| "components": [ | |
| "xgboost", | |
| "random_forest", | |
| "extra_trees", | |
| "vanilla_lstm", | |
| "tft" | |
| ], | |
| "weights_method": "optimized_l_bfgs_b", | |
| "weights_file": "models/ensemble/ensemble_weights.json", | |
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| "auxiliary_artifacts": { | |
| "re_rct_progression": { | |
| "display_name": "Re/Rct Progression Regressors", | |
| "family": "auxiliary", | |
| "algorithm": "LinearRegressionBundle", | |
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| "scalers": { | |
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| "tensorflow" | |
| ], | |
| "training_date": "2026-03-10", | |
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| "models": { | |
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| "figures": { | |
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| }, | |
| "generated_at_utc": "2026-03-10T18:10:44.760892+00:00" | |
| }, | |
| "verification": { | |
| "hash_algorithm": "sha256", | |
| "required": true, | |
| "notes": "Verify checksums before serving or deploying artifacts.", | |
| "last_verified_utc": "2026-03-10T18:10:44.760892+00:00" | |
| }, | |
| "engineered_features": { | |
| "capacity_retention": "Current capacity / initial capacity ratio", | |
| "cumulative_energy": "Cumulative energy throughput (Wh)", | |
| "dRe_dn": "Rate of change of electrolyte resistance per cycle", | |
| "dRct_dn": "Rate of change of charge-transfer resistance per cycle", | |
| "soh_rolling_mean": "Rolling mean SOH over 5-cycle window", | |
| "voltage_slope": "Slope of voltage curve during discharge" | |
| }, | |
| "improvements_over_v2": [ | |
| "Cross-battery grouped split eliminates data leakage", | |
| "18 features (6 new physics-informed) vs 12 in v2", | |
| "Proper NaN imputation (ffill/bfill/median vs fillna(0))", | |
| "Optimized hyperparameters for all classical models", | |
| "XGBoost R\u00b2 improved from 0.567 to 0.987" | |
| ] | |
| } | |