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.

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Emerging

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.

No commits in the last 6 months.

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.

battery-management energy-storage predictive-maintenance product-design condition-monitoring
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 13 / 25

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Stars

9

Forks

2

Language

Jupyter Notebook

License

MIT

Last pushed

Apr 14, 2025

Commits (30d)

0

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