Affiliation:
1. Fırat University
2. Manisa Celal Bayar University
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.
Publisher
Research Square Platform LLC
Reference50 articles.
1. “Power module electronics in HEV/EV applications: New trends in wide-bandgap semiconductor technologies and design aspects;Matallana A;Renewable and Sustainable Energy Reviews,2019
2. Risk management over the life cycle of lithium-ion batteries in electric vehicles;Christensen PA;Renewable and Sustainable Energy Reviews,2021
3. Critical review and functional safety of a battery management system for large-scale lithium-ion battery pack technologies;See KW;Int J Coal Sci Technol,2022
4. “A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements;Li Y;Appl Energy,2022
5. Z. Huang, Y. Fang, and J. Xu, “SOC Estimation of Li-ION Battery Based on Improved EKF Algorithm,” International Journal of Automotive Technology 2021 22:2, vol. 22, no. 2, pp. 335–340, Mar. 2021, doi: 10.1007/S12239-021-0032-4.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献