uw-mad-dash/Battery-SoC-Estimation
Data and code for the paper 'Estimating Battery State-of-Charge within 1% using Machine Learning and Physics-based Models' (SAE'23)
This project helps engineers and researchers accurately estimate the remaining charge in lithium-polymer (LiPo) batteries. It takes raw battery sensor data, like voltage, current, and temperature, and uses machine learning models to predict the battery's State-of-Charge (SoC) within 1% accuracy. This is ideal for battery engineers, electric vehicle designers, or power system managers who need precise battery monitoring.
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Use this if you need to develop highly accurate battery State-of-Charge estimation models for LiPo batteries using machine learning, and you have access to detailed sensor data from battery cycling.
Not ideal if you need a pre-built, ready-to-deploy solution for a different battery chemistry or if you lack the expertise to work with Python and neural networks.
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Jupyter Notebook
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Apache-2.0
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Last pushed
Mar 09, 2023
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