Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106185
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | en_US |
dc.creator | Waseem, M | en_US |
dc.creator | Huang, JY | en_US |
dc.creator | Wong, CN | en_US |
dc.creator | Lee, CKM | en_US |
dc.date.accessioned | 2024-05-03T00:45:40Z | - |
dc.date.available | 2024-05-03T00:45:40Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106185 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Waseem M, Huang J, Wong C-N, Lee CKM. Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management. Mathematics. 2023; 11(20):4263 is available at https://dx.doi.org/10.3390/math11204263. | en_US |
dc.subject | State of health estimation | en_US |
dc.subject | Lithium-ion batteries | en_US |
dc.subject | Electric vehicles | en_US |
dc.subject | Optimization | en_US |
dc.subject | Prognostics and health management | en_US |
dc.subject | Grey Wolf Optimizer | en_US |
dc.subject | Battery degradation | en_US |
dc.subject | Data-driven modeling | en_US |
dc.title | Data-driven GWO-BRNN-based SOH estimation of lithium-ion batteries in EVs for their prognostics and health management | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 11 | en_US |
dc.identifier.issue | 20 | en_US |
dc.identifier.doi | 10.3390/math11204263 | en_US |
dcterms.abstract | Due to the complexity of the aging process, maintaining the state of health (SOH) of lithium-ion batteries is a significant challenge that must be overcome. This study presents a new SOH estimation approach based on hybrid Grey Wolf Optimization (GWO) with Bayesian Regularized Neural Networks (BRNN). The approach utilizes health features (HFs) extracted from the battery charging-discharging process. Selected external voltage and current characteristics from the charging-discharging process serve as HFs to explain the aging mechanism of the batteries. The Pearson correlation coefficient, the Kendall rank correlation coefficient, and the Spearman rank correlation coefficient are then employed to select HFs that have a high degree of association with battery capacity. In this paper, GWO is introduced as a method for optimizing and selecting appropriate hyper-p parameters for BRNN. GWO-BRNN updates the population through mutation, crossover, and screening operations to obtain the globally optimal solution and improve the ability to conduct global searches. The validity of the proposed technique was assessed by examining the NASA battery dataset. Based on the simulation results, the presented approach demonstrates a higher level of accuracy. The proposed GWO-BRNN-based SOH estimation achieves estimate assessment indicators of less than 1%, significantly lower than the estimated results obtained by existing approaches. The proposed framework helps develop electric vehicle battery prognostics and health management for the widespread use of eco-friendly and reliable electric transportation. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Mathematics, Oct. 2023, v. 11, no. 20, 4263 | en_US |
dcterms.isPartOf | Mathematics | en_US |
dcterms.issued | 2023-10 | - |
dc.identifier.isi | WOS:001093686100001 | - |
dc.identifier.eissn | 2227-7390 | en_US |
dc.identifier.artn | 4263 | en_US |
dc.description.validate | 202405 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Centre for Advances in Reliability and Safety (CAiRS) admitted under AIR@InnoHK Research Cluster | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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mathematics-11-04263-v2.pdf | 11.44 MB | Adobe PDF | View/Open |
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