A New Lithium Polymer Battery Dataset with Different Discharge Levels: SOC Estimation of Lithium Polymer Batteries with Different Convolutional Neural Network Models

Abstract

In this study, a new dataset was created for use to estimate the state of charge (SOC) of lithium polymer batteries. A new experimental system was created to obtain the dataset by measuring the current, voltage, and temperature parameters of lithium polymer batteries. A convolutional neural network (CNN)-based deep learning model was used as the SOC prediction method. The effect of both batch size and dense network hyperparameter value on total parameter and deep learning error metric values for CNN-based lithium polymer battery SOC estimation is discussed. The proposed method, unlike deep learning models that require a high processing load in electronic cards, has provided remarkable results by being determined according to four different dense networks and two different batch size values. The proposed model has been obtained by performing experiments on optimizer, learning rate, dense network, and batch size values while determining the appropriate parameters to make successful predictions. The success of the CNN models was compared by conducting deep learning training on a computer with an Nvidia Gtx 1060 graphics card running the Ubuntu operating system. Adadelta optimizer achieved R2 0.977262 prediction success with learning rate 10(-2), batch size 5 x 10(2), dense 10(5) hyperparameter values. According to the results of the experiment, it was concluded that in the CNN deep learning method, large dense layers and small batch size values created less error in SOC estimation.