Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods

Abstract

In this study, the GWO-BiLSTM method has been proposed by successfully estimating the SOC with the BiLSTM deep learning method using the hyper-parameter values determined by the GWO method of the lithium polymer battery. EV, HEV, and robots are used more healthily with successful, reliable, and fast SOC estimation, which has an important place in the Battery Management System. In studies using deep learning methods, it is important to solve the problems of underfitting, overfitting, and estimation error by determining the hyper-parameters appropriately. Thus, this study aims to solve an important problem by investigating the problem of determining the hyperparameter values for the deep learning method with metaheuristic optimization methods. This study was designed to compare the prediction success of the BiLSTM method trained with the optimal hyperparameter values obtained by the GWO method with cutting-edge deep learning methods trained with hyperparameter values obtained by trial and error. The success of the proposed method was verified by comparing the cutting-edge data-based deep learning methods and the BiLSTM method with the SOC estimation MAE, MSE, RMSE, and Runtime(s) metrics. According to the findings obtained during the hyperparameter determination studies, it takes longer time to determine the hyperparameters by trial and error than to determine the hyperparameters by metaheuristic optimization method when estimating lithium battery SOC with the deep learning method. Also, the GWO-BiLSTM method was the most successful method with an RMSE of 0.09244% and an R2 of 0.9987 values according to the average results of SOC estimation made with the lithium polymer battery data set, which was created by experiments performed at different discharge levels and is new in the literature.