The Transactions of the Korean Institute of Power Electronics (전력전자학회논문지)

The Korean Institute of Power Electronics (전력전자학회)
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SOH Estimation and Feature Extraction using Principal Component Analysis based on Health Indicator for High Energy Battery Pack

건전성 지표 기반 주성분분석(PCA)을 적용한 고용량 배터리 팩의 열화 인자 추출 방법 및 SOH 진단 기법 연구

  • Lee, Pyeong-Yeon (Electrical Engineering, Chungnam National University) ;
  • Kwon, Sanguk (Electrical Engineering, Chungnam National University) ;
  • Kang, Deokhun (Electrical Engineering, Chungnam National University) ;
  • Han, Seungyun (Electrical Engineering, Chungnam National University) ;
  • Kim, Jonghoon (Electrical Engineering, Chungnam National University)
  • Received : 2020.01.24
  • Accepted : 2020.04.03
  • Published : 2020.10.20
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Abstract

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.

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