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Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model

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  • Zhelin Huang
  • Fangfang Yang
  • Xin Li

Abstract

Accurate prediction of battery quality using early-cycle data is critical for battery, especially lithium battery in microgrid networks. To effectively predict the lifetime of lithium-ion batteries, a time series classification method is proposed that classifies batteries into high-lifetime and low-lifetime groups using features extracted from early-cycle charge-discharge data. The proposed method is based on a smooth localized complex exponential model that can extract battery features from time-frequency maps and self-adaptively select the time-frequency resolution to maximize the discrepancy of data from the two groups. A smooth localized complex exponential periodogram is then calculated to obtain the time-frequency decomposition of the whole time series data for further classification. The experimental results show that, by using battery features extracted from the first 128 charge-discharge processes, the proposed method can accurately classify batteries into high-lifetime and low-lifetime groups, with classification accuracy and specificity as high as 95.12% and 92.5%, respectively.

Suggested Citation

  • Zhelin Huang & Fangfang Yang & Xin Li, 2021. "Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model," Complexity, Hindawi, vol. 2021, pages 1-10, January.
  • Handle: RePEc:hin:complx:6618708
    DOI: 10.1155/2021/6618708
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    Cited by:

    1. Débora B. S. Oliveira & Luna L. Glória & Rodrigo A. S. Kraemer & Alisson C. Silva & Douglas P. Dias & Alice C. Oliveira & Marcos A. I. Martins & Mathias A. Ludwig & Victor F. Gruner & Lenon Schmitz & , 2022. "Mixed-Integer Linear Programming Model to Assess Lithium-Ion Battery Degradation Cost," Energies, MDPI, vol. 15(9), pages 1-18, April.
    2. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.

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