Performance Comparison of Lithium Polymer Battery SOC Estimation Using GWO-BiLSTM and Cutting-Edge Deep Learning Methods

Abstract

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. 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. EV, HEV, and robots are used more healthily with the successful, reliable, and fast SOC estimation, which has an important place in the Battery Management System. 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. In the comparison, the prediction successes of the BiLSTM method, which was trained with the optimal hyper-parameter values obtained by the GWO method, with the cutting-edge deep learning methods trained with the hyper-parameter values obtained through trial and error were compared. The GWO-BiLSTM method was the most successful method with RMSE of 0.09244% and 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.