Hybrid Electric Vehicle Characteristics Change Analysis Using Mileage Interval Dataopen access
- Authors
- Woo, Jiyoung; Yang, Inbeom; Pyon, Chongun
- Issue Date
- Aug-2020
- Publisher
- MDPI
- Keywords
- hybrid electric vehicle (HEV); driving data; mileage interval; machine learning; characteristics change analysis
- Citation
- Applied Sciences-basel, v.10, no.16
- Journal Title
- Applied Sciences-basel
- Volume
- 10
- Number
- 16
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2597
- DOI
- 10.3390/app10165533
- ISSN
- 2076-3417
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - SCH Media Labs > Department of Smart Automobile > 1. Journal Articles
- SCH Media Labs > Department of Big Data Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.