대한기계학회논문집A (Transactions of the Korean Society of Mechanical Engineers A)

  • 제38권6호
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  • Pages.677-682
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  • 2014
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  • 1226-4873(pISSN)
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  • 2288-5226(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
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데이터 마이닝을 이용한 리튬 이차전지의 전류밀도 영향인자 분석

Design Analysis of Current Density in Lithium Secondary Battery Using Data Mining Techniques

  • 정동호 (연세대학교 기계공학부) ;
  • 이종수 (연세대학교 기계공학부) ;
  • 최하영 (동양미래대학교 기계공학부)
  • Jeong, Dong Ho (School of Mechanical Engineering, Yonsei Univ.,) ;
  • Lee, Jongsoo (School of Mechanical Engineering, Yonsei Univ.,) ;
  • Choi, Ha-Young (Dept. of Mechanical Engineering, Dongyang Mirae Univ.)
  • 투고 : 2014.01.28
  • 심사 : 2014.03.28
  • 발행 : 2014.06.01
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초록

본 연구에서는 데이터 마이닝의 방법인 의사결정나무와 인공신경망을 이용하여 리튬 이차전지의 전류밀도 특성에 대해 핵심 설계 인자를 도출하고 비교하였다. 먼저 의사결정나무-인공신경망 모델을 이용한 설계방법으로, 비선형성을 나타내는 초기 극판 설계인자들 중에 의사결정나무 모델을 통해 주요 설계 인자를 도출한 다음 인공신경망을 이용하여 설계인자들 간의 중요도와 전류밀도와의 가중치 분석을 수행하였다. 두 번째 방법은 인공신경망 모델만을 이용한 방법으로, 초기 설계인자들을 별도의 주요 인자 도출 과정 없이 모두 인공신경망을 구축하는데 사용하여 전류밀도와의 연관성 및 가중치를 분석하였다.

In the present study, a decision tree and artificial neural network were used to determine critical design parameters for lithium ion batteries and compare their performances. First, a design method that used a decision tree-artificial neural network model was used to determine the major design factors among early pole plate design factors that showed nonlinearity. Then, the artificial neural network was used to implement a weighted value analysis of the importance of the design factors and their effect on the current density. The second method involved the use of an artificial neural network model to construct artificial networks without separate determinations of the major early design factors to analyze the connections and weighted values related to the current density.

키워드

참고문헌

  1. http://corrosion.kaist.ac.kr/battery.htm
  2. Park, Y. J., Jun, S., Kim, S. and Lee, D. H., 2010, "Design Optimization of a Loop Heat Pipe to Cool a Lithium Ion Battery Onboard a Mmilitary Aaircraft," J. of Mechanical Science and Technology, Vol. 24, No. 2, pp.609-618. https://doi.org/10.1007/s12206-009-1214-6
  3. Besenhard, J. O., 1999, "Handbook of Battery Materials," Wiley-VCH, Weinheim.
  4. Peled, E., 1979, "The Electrochemical Behavior of Alkali and Alkaline Earth Metals in Nonaqueous Battery Systems? The Solid Electrolyte Interphase Model," J. of Electrochemical Society, Vol. 126, No. 12, pp. 2047-2051. https://doi.org/10.1149/1.2128859
  5. Yufit, V., Shearing, P., Hamilton, R. W., Lee, P. D., Wu, M. and Brandon, N. P., 2011, "Investigation of Lithium- Ion Polymer Battery Cell Failure Using X-ray Computed Tomography," Electrochemistry Communications, Vol. 13, No. 6, pp. 608-610. https://doi.org/10.1016/j.elecom.2011.03.022
  6. Bae, H. S., Cho, D. H., Suk, K. H., Kim, B. S., Choi, K. R., Lee, J. U., No, S. W., Lee, S. C. and Son, Y. H., 2004, "Data Mining Using SAS Enterprise Miner," KyoWoo.
  7. Lee, J., Jeong, H., Choi, D.-H., Volovoi, V. and Mavris, D., 2007, "An Enhancement of Constraint Feasibility in BPN Based Approximate Optimization," Computer Methods in Applied Mechanics and Engineering, Vol. 196, Issues 17-20, pp. 2147-2160. https://doi.org/10.1016/j.cma.2006.11.005
  8. Lee, J. and Hajela, P., 1996, "Parallel Genetic Algorithm Implementation in Multidisciplinary Rotor Blade Design," Journal of Aircraft, Vol. 33, No. 5, pp. 962-969. https://doi.org/10.2514/3.47042
  9. Cerrito, P. B., 2009, Introduction to Data Mining Using SAS Enterprise Miner, SAS Publishing.