Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine

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Abstract

Accurate knowledge of the state of health (SOH) is important for battery management system to maintain safe operation of batteries and extend their lifetime. In order to improve the accuracy in estimating the SOH of a battery, a new method based on empirical mode decomposition sample entropy (EMDSE) and support vector machine (SVM) is proposed in this paper. Compared with the tradition SE-based method, EMD is used to filter the noise of the original signal. Then the EMDSE as a new feature is used for the SVM training, and the potential relationship between the SOH and the EMDSE is established. Finally, the effectiveness of the proposed method is verified by experimental results.
Original languageEnglish
Title of host publication2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
Number of pages6
PublisherIEEE Press
Publication date2020
Pages3424-3429
Article number9124327
ISBN (Electronic)9781728148298
DOIs
Publication statusPublished - 2020
Event2020 IEEE Applied Power Electronics Conference and Exposition (APEC) - New Orleans, LA, United States
Duration: 15 Mar 202019 Mar 2020

Conference

Conference2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
CountryUnited States
CityNew Orleans, LA
Period15/03/202019/03/2020
SeriesIEEE Applied Power Electronics Conference and Exposition (APEC)
ISSN1048-2334

Keywords

  • Empirical mode decomposition denoising
  • Lithium-ion battery
  • Sample entropy
  • State of health

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