This paper proposes a neural network model for state-of-charge (SOC) estimation in lithium-ion battery cells. The proposed deep neural network model is a cycle-based recurrent model that leverages relevant information from historical cycles to provide reliable estimates of the state-of-charge of on-going cycles within a mean-absolute error (MAE) of 1%. In addition, the proposed model can be trained in a relatively short time. Details on the model followed by experimental verification are provided.


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    Title :

    A Cycle-based Recurrent Neural Network for State-of-Charge Estimation of Li-ion Battery Cells


    Contributors:


    Publication date :

    2020-06-01


    Size :

    198126 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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