krithicswaroopan/Lithium-ion_battery_SOH_Prediction
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.
This project helps operations engineers and product designers understand how lithium-ion batteries will degrade over time. By analyzing raw battery cycling data (like voltage, current, and temperature), it predicts the battery's State of Health (SOH) and State of Charge (SOC). The output is an accurate estimate of remaining battery life and performance, crucial for managing power systems in devices.
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Use this if you need to predict the degradation patterns and remaining useful life of lithium-ion batteries to optimize maintenance schedules or design more reliable products.
Not ideal if you are looking for real-time, on-device battery management system (BMS) software for immediate control, rather than predictive analysis.
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Jupyter Notebook
License
MIT
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Last pushed
Apr 14, 2025
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