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http://dx.doi.org/10.5050/KSNVE.2011.21.11.997

Fault Detection Algorithm of Charge-discharge System of Hybrid Electric Vehicle Using SVDD  

Na, Sang-Gun (고려대학교 대학원 제어계측공학과)
Yang, In-Beom (고려대학교 대학원 제어계측공학과, 자동차부품연구원 지능제어시스템연구센터)
Heo, Hoon (고려대학교 제어계측공학과)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.21, no.11, 2011 , pp. 997-1004 More about this Journal
Abstract
A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.
Keywords
SVDD(support vector data description); Fault Detection; One-class Classification; Incremental Data Learning; Hybrid Electric Vehicle; Data Reduction Technique;
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Times Cited By KSCI : 6  (Citation Analysis)
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