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

Zhelin Huang, Fangfang Yang and Xin Li

Complexity, 2021, vol. 2021, 1-10

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6618708

DOI: 10.1155/2021/6618708

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