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

Downloads

Download data is not yet available.
Abstract
138
PDF
71

Downloads

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

License

Copyright (c) 2022 Green University of Bangladesh

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish in the GUB Journal of Science and Engineering agree to the following terms that:

  1. Authors retain copyright and grant the GUB Journal of Science and Engineering the right of first publication of the work.
  2. image
    Articles in GUB Journal of Science and Engineering are licensed under a Creative Commons CC BY-NC-ND License Attribution-NonCommercial-NoDerivatives 4.0 International License. This license permits Share — copy and redistribute the material in any medium or format.

Copyright and Reprint Permissions

  1. Individual contributions contained in it are protected by the copyright of Green University of Bangladesh.
  2. Photocopies of this journal in full or parts for personal or classroom usage may be allowed provided that copies are not made or distributed for profit or commercial advantage and the copies bear this notice and the full citation.
  3. Copyright for components of this work owned by others must be honored. Abstracting with credit is permitted.
  4. Specific permission of the publisher and payment of a fee are required for multiple or systemic copying, advertising or promotional purposes, resale, republishing, posting on servers, redistributing to lists and all forms of document delivery.
  5. Subscribers may reproduce a table of contents or prepare lists of articles including abstracts for internal circulation within their institutions.
  6. Permission of the Publisher is required for resale and distribution outside the institution. Permission of the publisher is required for all other derivative works, including compilations and translations.
  7. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, such as electronic, mechanical, photocopying, magnetic recording or otherwise, without prior written permission of the publisher.
  8. Permissions may be sought directly from the office of the executive editor of GUB Journal of Science and Engineering through E-mail at gubjse@fse.green.edu.bd.

Notice

  • Responsibility for the contents of an article rests upon the author(s) and not upon the editor or the publisher. Therefore, on responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, product instructions or ideas contained in the material herein.