Earticle

초록

영어

In order to solve the problems that lithium-ion power battery cannot reflect state of health(SOH) in sorting process , the parameters which can reflect the battery SOH, such as capacity, AC resistance and self-discharge current, were used as the input vector of battery sorting model, fuzzy c-means clustering analysis and support vector machine based on cross validation algorithm to the battery for classification and recognition were used, and lithium power battery sorting model was established and the same batch of power battery were separating tested and according to the experimental results, batteries was divided into groups. The test results showed that: battery electrochemistry was having a good consistency. The variation of capacitance was less than 5% while there was 1500 cycle life.

목차

Abstract
 1. Introduction
 2. Battery Classification Model
  2.1. Battery Parameter Selection
  2.2. Establishment of FCM-SVM classification model
 3. Verification of Classification Experiment
  3.1. Data collection
  3.2. Model Training and Simulation
 4. Experimental Verification
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Yu Zhilong [ School of Automation, Harbin University of Science and Technology, Harbin, China ]
• Li Ran [ School of Automation, Harbin University of Science and Technology, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605