Long-term Driving Data Analysis of Hybrid Electric Vehicle

Jiyoung Woo; Yang In Beom

doi:https://doi.org/10.9708/jksci.2018.23.03.063

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2018, 23(3), pp.63-70
DOI :https://doi.org/10.9708/jksci.2018.23.03.063
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Jiyoung Woo ¹ , Yang In Beom ²

¹ 순천향대학교
² 순천향대학교

ABSTRACT

In this work, we analyze the relationship between the accumulated mileage of hybrid electric vehicle(HEV) and the data provided from vehicle parts. Data were collected while traveling over 70,000 Km in various paths. The data collected in seconds are aggregated for 10 minutes and characterized in terms of centrality, variability, normality, and so on. We examined whether the statistical properties of vehicle parts are different for each cumulative mileage interval of a hybrid car. When the cumulative mileage interval is categorized into =< 30,000, <= 50,000, and >50,000, the statistical properties are classified by the mileage interval as 82.3% accuracy. This indicates that if the data of the vehicle parts is collected by operating the hybrid vehicle for 10 minutes, the cumulative mileage interval of the vehicle can be estimated. This makes it possible to detect the abnormality of the vehicle part relative to the accumulated mileage. It can be used to detect abnormal aging of vehicle parts and to inform maintenance necessity.
KEYWORDS

Hybrid electric vehicle , OBD-II , Driving data , Mileage , Machine learning
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KCI Citation (0)

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