Enhanced SOC estimation in Lithium-ion Batteries using GRU Neural Networks with GSA

Authors

  • Molla Shahadat Hossain Lipu Department of EEE, Green University of Bangladesh, Dhaka, Bangladesh
  • Tuhibur Rahman Department of EEE, Qassim University, raydah, Saudi Arabia
  • Naushad Ali Department of EEE, Green University of Bangladesh, Dhaka, Bangladesh
  • Shaheer Ansari Department of EEE, Universiti Kebangsaan Malaysia, Bangi, Malaysia
  • Md Sultan Mahmood Department of EEE, Nagoya University, Nagoya, Japan

DOI:

https://doi.org/10.3329/gubjse.v9i1.74879

Keywords:

Electric Vehicles, Lithium-ion Battery, State of Charge, Algorithm, Optimization

Abstract

State of charge (SOC) is one of the most important metrics to evaluate the performance of the battery management system (BMS) in electric vehicles (EVs). Lithium-ion batteries have been utilized often for SOC estimates in EV applications because of its attractive characteristics such as extended lifespans, high voltages, energy, and capacities. However, dynamic charging and discharging profiles, material degradation, battery aging, chemical reaction, and temperature fluctuations would diverge the accuracy of SOC estimation. Deep learning has proven to become an efficient method for SOC estimation in EVs due to its strong computational capabilities, enhanced generalization performance, and excellent accuracy under dynamic loading profiles. Therefore, this study proposes the use of the gated recurrent unit neural network (GRUNN) for estimating SOC in lithium-ion batteries. The best hyperparameters of GRUNN that enable improving SOC accuracy are found using gravitational search  optimization (GSA). To evaluate the functional sustainability of the suggested method, a full battery test bench model is created and accordingly, robustness of the proposed approach is verified under diverse EV drive cycles and aging impacts. The effectiveness of the proposed approach is evaluated by comparing with several existing approaches. The results demonstrate that the suggested method archives SOC error and RMSE in EV driving cycles and ageing effects below 5% and 2%, respectively. The excellent outcomes obtained by the proposed method would certainly enhance the battery charging and discharging profile and EV performance.

GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 1-12

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Published

2024-07-13

How to Cite

Lipu, M. S. H., Rahman, T., Ali, N., Ansari, S., & Mahmood, M. S. (2024). Enhanced SOC estimation in Lithium-ion Batteries using GRU Neural Networks with GSA. GUB Journal of Science and Engineering, 9(1), 1–12. https://doi.org/10.3329/gubjse.v9i1.74879

Issue

Vol. 9 No. 1 (2022)

Section

Articles

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