Transactions of the Korean Society for Noise and Vibration Engineering (한국소음진동공학회논문집)

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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Fault Detection Algorithm of Charge-discharge System of Hybrid Electric Vehicle Using SVDD

SVDD기법을 이용한 하이브리드 전기자동차 충-방전시스템의 고장검출 알고리듬

  • 나상건 (고려대학교 대학원 제어계측공학과) ;
  • 양인범 (고려대학교 대학원 제어계측공학과, 자동차부품연구원 지능제어시스템연구센터) ;
  • 허훈 (고려대학교 제어계측공학과)
  • Received : 2011.05.02
  • Accepted : 2011.10.17
  • Published : 2011.11.20
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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.

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References

  1. Na, S. G., Lee, Y. H., Kim, J. S., Park, S. M. and Heo, H., 2010, A Study on the Hybrid Electric Vehicle Fault Diagnosis Algorithm, The Korean Society of Automotive Engineers Conference, pp. 2935-2942.
  2. Joo, K. J., Jang, S. R., Mostafa, F. K. A. and Rim, G. H., 2009, Technical Trend of Electric Vehicle, The Korean Institute of Electrical Engineers, pp. 947-948.
  3. Park, S. G., Park, W. S., Lee, H. J., Hong, W. G. and Oh, J. E., 2007, Fault Signal Analysis of the Automotive Components Using Experimental Method, Part 1-consideration of the Engine Signals, Proceedings of the KSNVE Annual Autumn Conference, pp. 238-242.
  4. Park, S. G., Park, W. S., Lee, Y. Y., Kim, D. S. and Oh, J. E., 2008, A Fault Diagnosis on the Rotating Machinery Using MTS, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 18, No. 6, pp. 619-623. https://doi.org/10.5050/KSNVN.2008.18.6.619
  5. Lee, K. H., Kong, B. H., Cheung, W. S. and Lee, S. G., 2009, System-diagnosis Algorithm by Using MPCA and ICA, Proceedings of the KSNVE Annual Autumn Conference, pp. 213-214.
  6. Ko, K. W., Oh, Y. S., Jung, Q. Y. and Heo, H., 2003, Abnormal Diagnostics of Vibration System Using SVM, Proceedings of the KSNVE Annual Spring Conference, pp. 932-937.
  7. Park, J. Y. and Leem, C. H., 2003, Support Vector Learning for Abnormality Detection Problems, Korean Institute of Intelligent Systems, Vol. 13, No. 3, pp. 266-274. https://doi.org/10.5391/JKIIS.2003.13.3.266
  8. Kang, D. S. and Park, J. Y., 2006, Pattern Denoising based on One-class Support Vector Machines, M. S. Thesis, Korea University.
  9. Na, S. G., Jeon, J. H., Han, I. J. and Heo, H., 2011, Fault Detection Algorithm of Hybrid Electric Vehicle Using SVDD, Proceedings of the KSNVE Annual Spring Conference, pp. 224-229.
  10. Na, S. G., Han, I. J. and Heo, H., 2011, Fault Detection Algorithm Using SVDD and Data Reduction Techniques for Battery of Hybrid Electric Vehicle, Computer Applications and Network Security Spring Conference, pp. 293-297.
  11. Kim, H. G., Heo, S. J. and Kang, G. B., 2009, Modeling and Characteristic Analysis of HEV Li-ion Battery Using Recursive Least Square Estimation, Transactions of KSAE, Vol. 17, No. 1, pp. 130-136.

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