Poster

Physics-informed machine learning for electrochemical state estimation in lithium-ion batteries

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Abstract

This work presents a hybrid state estimation model by incorporating physics-based modeling into a data-driven approach. An experimentally validated high-fidelity model is employed to generate big data training data for a comprehensive operating condition matrix. This data is used to train a deep neural network to estimate the internal electrochemical states in the electrode and the electrolyte with high accuracy and low computational cost.

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