sileneer/NRP_2022_EEE12
LSTM and GRU model to predict the SOH of the batteries
This project offers a straightforward way to predict the State-of-Health (SOH) for Li-Ion batteries using historical degradation data. It takes raw battery usage data as input and provides highly accurate SOH predictions, helping engineers and researchers monitor battery longevity and performance. Its target users are those involved in battery management, research, or product development who need to understand battery degradation.
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Use this if you need an accurate, data-driven method to predict the remaining useful life of Li-Ion batteries without complex model development.
Not ideal if you require real-time, edge-device deployment or prefer a non-machine learning approach for SOH estimation.
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70
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10
Language
Jupyter Notebook
License
MIT
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Last pushed
Aug 22, 2025
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