Use of Deep Neural Networks to Predict Lithium-Ion Cell Voltages During Charging and Discharging

Abstract

This paper uses machine learning to predict either the voltage, current, or state of charge (SOC) during a discharging of a Li-Ion cell. Due to the non-linear characteristics of a Li-Ion cells, machine learning is used to create a model for prediction. Predicting the Li-Ion cell characteristics is useful for products in determining the end of discharge (EOD) levels under different loading conditions. The model used is a Deep Neural Network (DNN) with 2 hidden layers with 128 nodes each and is implemented using Python. To train the model, approximately 6000 data points of charging and discharging data of a LCO prismatic CS2 1100mAh Li-Ion cell is used. It was found that the accuracy of the model is approximately 5% and worsens at lower SOC.