Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (2): 38-44.doi: 10.16180/j.cnki.issn1007-7820.2021.02.007

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Research on Sorting of Retired Lithium Battery Based on Radial Basis Function Neural Network

HE Zhonglin,ZHOU Ping   

  1. School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-11-25 Online:2021-02-15 Published:2021-01-22
  • Supported by:
    National Natural Science Foundation of China(51877138);Shanghai Science and Technology Development Fund(19QA1406200);“Chenguang Program” Supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission(16CG52)


With the aging of lithium-ion batteries of electric vehicles in the near future, research on the second use of retired LIBs has become more and more critical. However, the classification method of the retired LIBs is challenging before the second use due to large cell variations. The traditional sorting method requires a single battery to be tested one by one to complete the sorting, but this method is not suitable for rapid sorting of large-scale batteries. In order to improve the sorting speed of the retired battery, the equalization-charging sorting method is proposed in this study. The batteries to be sorted are parallel-balanced, and the constant current charging is performed after the voltage is consistent. According to the characteristics of different aging batteries with different voltage curves, combined with the non-linear function approximation capability of radial basis function neural network, battery capacity estimation are realized through model training, thereby completing battery sorting. The results of the simulation verification reveale that the capacity error doesnot exceed ±5%, and the results of the experimental verification show the capacity error is within ±3%, which indicates the proposed method can achieve the sorting of retired lithium batteries.

Key words: retired lithium-ion battery, rapid classification, parallel equalization, constant current charging, radial basis function neural network, capacity estimation

CLC Number: 

  • TP391.9