Explainable Data-Driven Digital Twins for Predicting Battery States in Electric Vehicles
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Njoku, Judith Nkechinyere | - |
dc.contributor.author | Ifeanyi Nwakanma, Cosmas | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.date.accessioned | 2024-07-17T08:00:59Z | - |
dc.date.available | 2024-07-17T08:00:59Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28792 | - |
dc.description.abstract | Advancements in battery management systems (BMS) involve using digital twins to optimize battery performance in electric vehicles. The state of charge and health estimations are essential for battery efficiency and longevity. Digital twins allow for precise predictions of the state of charge and state of health by simulating battery behavior under different conditions. Using artificial intelligence (AI) in digital twins improves predictive capabilities, as demonstrated through studies employing deep neural networks (DNN) and long short-term memory networks (LSTM). However, incorporating AI presents challenges due to the opaque nature of the models, necessitating the need for explainable artificial intelligence (XAI) and trustworthy digital twin models. This study pioneered XAI methods such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and linear regression-based surrogate models to explain the predictions of DNNs and LSTMs in digital twin-supported BMSs. The results reveal that the DNN and LSTM digital twin models are more reliable for state-of-health and state-of-charge estimation due to higher $R<^>{2}$ scores, lower mean residuals, and better XAI results. | - |
dc.format.extent | 22 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Explainable Data-Driven Digital Twins for Predicting Battery States in Electric Vehicles | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2024.3413075 | - |
dc.identifier.scopusid | 2-s2.0-85196064444 | - |
dc.identifier.wosid | 001251488200001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.12, pp 83480 - 83501 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 12 | - |
dc.citation.startPage | 83480 | - |
dc.citation.endPage | 83501 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | OF-HEALTH ESTIMATION | - |
dc.subject.keywordPlus | CHARGE ESTIMATION | - |
dc.subject.keywordAuthor | Batteries | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Predictive models | - |
dc.subject.keywordAuthor | Digital twins | - |
dc.subject.keywordAuthor | Electric vehicles | - |
dc.subject.keywordAuthor | Long short term memory | - |
dc.subject.keywordAuthor | Battery management systems | - |
dc.subject.keywordAuthor | Explainable AI | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | digital twins | - |
dc.subject.keywordAuthor | artificial intelligence | - |
dc.subject.keywordAuthor | XAI | - |
dc.subject.keywordAuthor | explainable artificial intelligence | - |
dc.subject.keywordAuthor | machine learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.