Hybrid Electric Vehicle Characteristics Change Analysis Using Mileage Interval Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Woo, Jiyoung | - |
dc.contributor.author | Yang, Inbeom | - |
dc.contributor.author | Pyon, Chongun | - |
dc.date.accessioned | 2021-08-11T08:34:03Z | - |
dc.date.available | 2021-08-11T08:34:03Z | - |
dc.date.issued | 2020-08 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2597 | - |
dc.description.abstract | In this work, the relationship between the accumulated mileage of a hybrid electric vehicle (HEV) and the data provided from vehicle parts has been analyzed. Data were collected while traveling over 70,000 km in various paths. The collected data were aggregated for 10 min and characterized in terms of centrality and variability. It has been examined whether the statistical properties of vehicle parts are different for each cumulative mileage interval. When the cumulative mileage interval is categorized into 30,000-50,000, 50,000-60,000, and 60,000-70,000, the statistical properties contributed in classifying the mileage interval with accuracy of 92.68%, 82.58%, and 80.65%, respectively. This indicates that if the data of the vehicle parts are collected by operating the HEV for 10 min, the cumulative mileage interval of the vehicle can be estimated. This makes it possible to detect abnormality or characteristics change in the vehicle parts relative to the accumulated mileage. It also can be used to detect abnormal aging of vehicle parts and to inform maintenance necessity. Furthermore, a part or module that has a significant change in characteristics according to the mileage interval has been identified. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Hybrid Electric Vehicle Characteristics Change Analysis Using Mileage Interval Data | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/app10165533 | - |
dc.identifier.scopusid | 2-s2.0-85089806959 | - |
dc.identifier.wosid | 000567291100001 | - |
dc.identifier.bibliographicCitation | Applied Sciences-basel, v.10, no.16 | - |
dc.citation.title | Applied Sciences-basel | - |
dc.citation.volume | 10 | - |
dc.citation.number | 16 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordPlus | HEALTH ESTIMATION | - |
dc.subject.keywordPlus | DRIVING BEHAVIOR | - |
dc.subject.keywordPlus | BATTERIES | - |
dc.subject.keywordPlus | STATE | - |
dc.subject.keywordAuthor | hybrid electric vehicle (HEV) | - |
dc.subject.keywordAuthor | driving data | - |
dc.subject.keywordAuthor | mileage interval | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | characteristics change analysis | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG 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.