The project analyzes battery cycling data to predict degradation patterns and performance metrics using both deep learning (LSTM) and traditional machine learning (XGBoost) approaches. The implementation enables accurate estimation of battery health, which is crucial for battery management systems in various applications.
machine-learning deep-learning python3 lstm-model time-series-analysis xgboost-model predictive-maintenance lithium-ion-batteries battery-degradation state-of-health battery-soh battery-health-prediction nasa-battery-dataset battery-lifecycle-prognostics battery-analytics
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Updated
Apr 14, 2025 - Jupyter Notebook