Kadem, O., Candan, H. and Kim, J. orcid.org/0000-0002-3456-6614 (2024) Hybrid Deep Neural Network for Electric Vehicle State of Charge Estimation. In: 2024 IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES). 2024 IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES), 21-22 Jun 2024, Bhopal, India. IEEE ISBN 979-8-3503-9073-5
Abstract
In electric vehicles (EVs), a battery management system plays a critical role in ensuring the reliable and safe operation of batteries. One of its main tasks is to display the battery state of charge (SoC), reflecting the battery's current available charge level. However, the accuracy of SoC estimation is a formidable challenge due to the intricate nature of battery modelling. To overcome this challenge, data-driven methods have recently emerged as the dominant approach for SoC estimation. Considering the SoC estimation problem as a time series problem, we propose a hybrid deep neural network (DNN) that eliminates the need for feature engineering or adaptive filtering. The proposed DNN incorporates a convolutional layer, a long short-term memory layer, and a dense layer. The DNN was trained using data collected from benchmark EV driving cycles (DST, BJDST, and FUDS drive cycles) within a temperature range of 0 °C to 50 °C. The performance evaluation of the trained DNN has been carried out using another standard EV driving cycle (US06 drive cycle) at various operating temperatures. The results demonstrate that the trained DNN effectively captures the dynamic behaviour of the battery under various operational conditions, yielding a maximum percentage SoC estimation error of approximately 3%. Furthermore, the results indicate that the proposed DNN technique is capable of generalising the battery's dynamic response to unseen data. Overall, our findings show that the proposed technique is promising for EV applications in which battery operating conditions exhibit significant variability.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Convolutional neural network, Long short term memory, Battery management system, Electrical battery, State of charge estimation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Dec 2024 10:48 |
Last Modified: | 06 Dec 2024 10:51 |
Published Version: | https://ieeexplore.ieee.org/document/10653585 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icepes60647.2024.10653585 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220530 |