Long-term Driving Data Analysis of Hybrid Electric Vehicle

Jiyoung Woo; Yang In Beom

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2018, 23(3), pp.63-70
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Jiyoung Woo ¹, Yang In Beom ²

¹순천향대학교
²순천향대학교

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
REFERENCES (12)

[web] / 0000 / Vehicle Technologies Program : Advanced Vehicle Testing Activity Site / http://www1.eere.energy.gov/vehiclesandfuels/avta/

[web] / 0000 / Hydrogen Program / http://www.hydrogen.energy.gov

[web] / 0000 / HyFLEET CUTE Project / http://www.h2moves.eu

[journal] T. Wakita / 2006 / Driver identification using driving behavior signals / IEICE TRANSACTIONS on Information and Systems 89(3) : 1188~1194

[journal] C. Miyajima / 2007 / Driver modeling based on driving behavior and its evaluation in driver identification / Proceedings of the IEEE 95(2) : 427~437 / 10.1109/JPROC.2006.888405

[book] Y. Nishiwaki / 2007 / Driver identification based on spectral analysis of driving behavioral signals, in Advances for In-Vehicle and Mobile Systems / Springer : 25~34

[confproc] B. I. Kwak / 2016 / Know your master: Driver profiling-based anti-theft method / Privacy, Security and Trust (PST), 2016 14th Annual Conference on. IEEE

[confproc] X. Meng / 2006 / Human driving behavior recognition based on hidden markov models / Robotics and Biomimetics, 2006. ROBIO'06. IEEE International Conference on. IEEE : 274~279

[confproc] S. Choi / 2007 / Analysis and classification of driver behavior using in-vehicle can-bus information / Biennial Workshop on DSP for In-Vehicle and Mobile Systems : 17~19

[journal] A. Wahab / 2009 / Driving profile modeling and recognition based on soft computing approach / Neural Networks, IEEE Transactions on 20(4) : 563~582 / 10.1109/TNN.2008.2007906

[journal] G. Kedar-Dongarkar. / 2012 / Driver classification for optimization of energy usage in a vehicle / Procedia Computer Science 8 : 388~393 / 10.1016/j.procs.2012.01.077

[journal] M. Enev / 2016 / Automobile driver fingerprinting / Proceedings on Privacy Enhancing Technologies 2016(1) : 34~50
KCI Citation (0)

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