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State-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Network

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dc.citedby4
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHow D.N.T.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorMuttaqi K.M.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57212923888en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid55582332500en_US
dc.date.accessioned2023-05-29T08:07:17Z
dc.date.available2023-05-29T08:07:17Z
dc.date.issued2020
dc.descriptionCharging (batteries); Deep learning; Learning systems; Lithium compounds; Lithium-ion batteries; Long short-term memory; Multilayer neural networks; Temperature; Empirical evaluations; Experiment set-up; Hyper-parameter; Learning methods; Multi layer perceptron; Simple recurrent networks; State of charge; State-of-charge estimation; Battery management systemsen_US
dc.description.abstractThe state of charge (SOC) is a crucial indicator of a Li-ion battery management system (BMS). A BMS with a good SOC assessment can dramatically improve the lifespan of the battery and ensure the safety of the end-user. With deep learning making tremendous strides in many other fields, this study aims to provide an empirical evaluation of commonly used deep learning methods on the task of SOC estimation. We propose the use of two-hidden-layer gated recurrent units (GRU) to estimate the SOC at various ambient temperatures. In this work, we conducted two experiment setups to showcase the capability of the proposed GRU model. In the first setup, the GRU was trained on the DST, BJDST and US06 drive cycle and evaluated the FUDS drive cycle upon convergence. The same procedure was repeated with the second setup except the GRU was trained on the DST, BJDST and FUDS drive cycle and evaluated on the US06 drive cycle. In both experiment setups, the proposed GRU was evaluated on a novel drive cycle that it has not encountered during the training phase. We show that a two-hidden-layer GRU with appropriate hyperparameter combination and training methodology can reliably estimate the SOC of novel drive cycles at various ambient temperatures in comparison with other deep learning methods such as simple recurrent network (SRNN), Long Short-Term Memory (LSTM), 1D Residual Network (Resnet), 1D Visual Geometry Group Network (VGG) and the Multilayer Perceptron (MLP). The proposed GRU achieves 2.3% RMSE on the FUDS drive cycle and 1.2% RMSE on the US06 drive cycle outperforming all other models. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9334824
dc.identifier.doi10.1109/IAS44978.2020.9334824
dc.identifier.scopus2-s2.0-85101014266
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85101014266&doi=10.1109%2fIAS44978.2020.9334824&partnerID=40&md5=8bb1c1dde6d13d9305be8529b5d145ef
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25197
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 IEEE Industry Applications Society Annual Meeting, IAS 2020
dc.titleState-of-Charge Estimation of Li-ion Battery at Variable Ambient Temperature with Gated Recurrent Unit Networken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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