Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90962
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | en_US |
dc.creator | Zhang, Z | en_US |
dc.creator | Dong, Z | en_US |
dc.creator | Lin, H | en_US |
dc.creator | He, Z | en_US |
dc.creator | Wang, M | en_US |
dc.creator | He, Y | en_US |
dc.creator | Gao, X | en_US |
dc.creator | Gao, M | en_US |
dc.date.accessioned | 2021-09-03T02:35:41Z | - |
dc.date.available | 2021-09-03T02:35:41Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/90962 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. | en_US |
dc.rights | The following publication Zhang, Z., Dong, Z., Lin, H., He, Z., Wang, M., He, Y., ... & Gao, M. (2021). An Improved Bidirectional Gated Recurrent Unit Method for Accurate State-of-Charge Estimation. IEEE Access, 9, 11252-11263 is available at https://doi.org/10.1109/ACCESS.2021.3049944. | en_US |
dc.subject | Bidirectional Gated Recurrent Unit | en_US |
dc.subject | Lithium-ion batteries | en_US |
dc.subject | Nesterov Accelerated Gradient | en_US |
dc.subject | State-of-Charge estimation | en_US |
dc.title | An improved bidirectional gated recurrent unit method for accurate state-of-charge estimation | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 11252 | en_US |
dc.identifier.epage | 11263 | en_US |
dc.identifier.volume | 9 | en_US |
dc.identifier.doi | 10.1109/ACCESS.2021.3049944 | en_US |
dcterms.abstract | State-of-Charge (SOC) estimation of lithium-ion batteries have a great significance for ensuring the safety and reliability of battery management systems in electrical vehicle. Deep learning method can hierarchically extract complex feature information from input data by building deep neural networks (DNNs) with multi-layer nonlinear transformations. With the development of graphic processing unit, the training speed of the network is faster than before, and it has been proved to be an effective data-driven method to estimate SOC. In order to further explore the potential of DNNs in SOC estimation, take battery measurements like voltage, current and temperature directly as input and SOC as output, an improved method using the Nesterov Accelerated Gradient (NAG) algorithm based Bidirectional Gated Recurrent Unit (Bi-GRU) network is put forward in this paper. Notably, to address the oscillation problem existing in the traditional gradient descent algorithm, NAG is used to optimize the Bi-GRU. The gradient update direction is corrected by considering the gradient influence of the historical and the current moment, combined with the estimated location of the parameters at the next moment. Compared to state-of-the-art estimation methods, the proposed method enables to capture battery temporal information in both forward and backward directions and get independent context information. Finally, two well-recognized lithium-ion batteries datasets from University of Maryland and McMaster University are applied to verify the validity of the research. Compared with the previous methods, the experimental results demonstrate that the proposed NAG based Bi-GRU method for SOC estimation can improve the precision of the prediction at various ambient temperature. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2021, v. 9, 9320579, p. 11252-11263 | en_US |
dcterms.isPartOf | IEEE access | en_US |
dcterms.issued | 2021 | - |
dc.identifier.scopus | 2-s2.0-85099572630 | - |
dc.identifier.eissn | 2169-3536 | en_US |
dc.identifier.artn | 9320579 | en_US |
dc.description.validate | 202109 bcvc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhang_Improved_Bidirectional_Gated.pdf | 2.1 MB | Adobe PDF | View/Open |
Page views
48
Last Week
1
1
Last month
Citations as of May 19, 2024
Downloads
30
Citations as of May 19, 2024
SCOPUSTM
Citations
36
Citations as of May 16, 2024
WEB OF SCIENCETM
Citations
29
Citations as of May 16, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.