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Hybrid Electric Vehicle Characteristics Change Analysis Using Mileage Interval Dataopen access

Authors
Woo, JiyoungYang, InbeomPyon, 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.
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SCH Media Labs > Department of Smart Automobile > 1. Journal Articles
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