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State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy

Charging (batteries); Deep learning; Digital arithmetic; Electric traction; Ions; Learning systems; Lithium compounds; Lithium-ion batteries; Statistical tests; Comparative evaluations; Feed-forward architectures; Floating point operations per seconds; Generalization capability; Lightweight batterie...

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Main Authors: Hannan M.A., How D.N.T., Mansor M.B., Hossain Lipu M.S., Ker P., Muttaqi K.
Other Authors: 7103014445
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-262282023-05-29T17:08:01Z State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy Hannan M.A. How D.N.T. Mansor M.B. Hossain Lipu M.S. Ker P. Muttaqi K. 7103014445 57212923888 6701749037 36518949700 37461740800 55582332500 Charging (batteries); Deep learning; Digital arithmetic; Electric traction; Ions; Learning systems; Lithium compounds; Lithium-ion batteries; Statistical tests; Comparative evaluations; Feed-forward architectures; Floating point operations per seconds; Generalization capability; Lightweight batteries; Model computation; State-of-charge estimation; Vehicle applications; Battery management systems Deep learning has gained much traction in application to state-of-charge (SOC) estimation for Li-ion batteries in electric vehicle applications. However, with the vast selection of architectures and hyperparameter combinations, it remains challenging to design an accurate and robust SOC estimation model with a sufficiently low computation cost. Therefore, this study provides a comparative evaluation among commonly used deep learning models from the recurrent, convolutional, and feedforward architecture benchmarked on an openly available Li-ion battery dataset. To evaluate model robustness and generalization capability, we train and test models on different drive cycles at various temperatures and compute the root mean squared error (RMSE) and mean absolute error metric. To evaluate model computation costs, we run models in real-time and record the model size, floating-point operations per second (FLOPS), and run-time duration per datapoint. This study proposes a two-hidden layer stacked gated recurrent unit model trained with a one-cycle policy learning rate scheduler. The proposed model achieves a minimum RMSE of 0.52% on the train dataset and 0.65% on the test dataset while maintaining a relatively low computation cost. Executing the proposed model in real-time takes up approximately 1 MB in disk space, 300K FLOPS, and 0.03 ms run-time per datapoint. This makes the proposed model feasible to be executed on lightweight battery management system processors. � 1972-2012 IEEE. Final 2023-05-29T09:08:01Z 2023-05-29T09:08:01Z 2021 Article 10.1109/TIA.2021.3065194 2-s2.0-85102685878 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102685878&doi=10.1109%2fTIA.2021.3065194&partnerID=40&md5=e4ee8c69f2cf6b14cd0cd536bdc38879 https://irepository.uniten.edu.my/handle/123456789/26228 57 3 9376269 2964 2971 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Charging (batteries); Deep learning; Digital arithmetic; Electric traction; Ions; Learning systems; Lithium compounds; Lithium-ion batteries; Statistical tests; Comparative evaluations; Feed-forward architectures; Floating point operations per seconds; Generalization capability; Lightweight batteries; Model computation; State-of-charge estimation; Vehicle applications; Battery management systems
author2 7103014445
author_facet 7103014445
Hannan M.A.
How D.N.T.
Mansor M.B.
Hossain Lipu M.S.
Ker P.
Muttaqi K.
format Article
author Hannan M.A.
How D.N.T.
Mansor M.B.
Hossain Lipu M.S.
Ker P.
Muttaqi K.
spellingShingle Hannan M.A.
How D.N.T.
Mansor M.B.
Hossain Lipu M.S.
Ker P.
Muttaqi K.
State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy
author_sort Hannan M.A.
title State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy
title_short State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy
title_full State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy
title_fullStr State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy
title_full_unstemmed State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit with One-Cycle Learning Rate Policy
title_sort state-of-charge estimation of li-ion battery using gated recurrent unit with one-cycle learning rate policy
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424104891318272
score 13.222552

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