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A Novel Battery State of Health Estimation Method Based on Outlier Detection Algorithm

  • Piao, Chang-hao (Institution of Pattern Recognition and Application, Chongqing University of Posts and Telecommunications) ;
  • Hu, Zi-hao (Institution of Pattern Recognition and Application, Chongqing University of Posts and Telecommunications) ;
  • Su, Ling (Chongqing Changan New Energy Automobile CO., LTD.) ;
  • Zhao, Jian-fei (Institution of Pattern Recognition and Application, Chongqing University of Posts and Telecommunications)
  • Received : 2015.04.28
  • Accepted : 2016.06.13
  • Published : 2016.11.01

Abstract

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

Acknowledgement

Supported by : CQ CSTC

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