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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.13 No.2
- 발행연도
- 2018.3
- 수록면
- 733 - 741 (9page)
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초록· 키워드
오류제보하기
Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it"s quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it’s found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.
목차
Abstract
1. Introduction
2. Relevance Vector Machine
3. Feature Extraction
4. Capacity Estimation
5. Conclusion
References
참고문헌 (43)
참고문헌 신청
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H. Cai and M. Xu, “Greenhouse Gas Implications of Fleet Electrification Based on Big Data-Informed Individual Travel Patterns,” Environ Sci Technol, vol. 47, no. 16, pp. 9035-43, Jul. 2013.
A. P. Schmidt, M. Bitzer, A. W. Imre, and L. Guzzella, “Model-Based Distinction and Quantification of Capacity Loss and Rate Capability Fade in Li-Ion Batteries,” Journal of Power Sources, vol. 195, no. 22, pp. 7634-7638, 2010.
M. V. Micea, L. Ungurean, G. N. Ca?rstoiu, and V. Groza, “Online State-of-Health Assessment for Battery Management Systems,” IEEE Trans. on Instrumentation and Measurement, vol. 60, no. 6, pp. 1997- 2006, Mar. 2011.
W. He, N. Williard, M. Osterman, and M. Pecht, “Prognostics of Lithium-ion Batteries based on Dempster-Shafer Theory and The Bayesian Monte Carlo Method,” Journal of Power Sources, vol. 196, no. 23, pp. 10314-10321, Dec. 2011.
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