Title: Research on lithium battery sorting method based on image adaptive recognition of feature points

Authors: Yongqin Zhou; Yubin Wang; Yujia Chang; Ce Huang; Ran Li

Addresses: Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China ' School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China ' State Grid Harbin Electric Power Supply Company, Harbin, Heilongjiang 150010, China ' School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China ' Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China

Abstract: When sorting batteries in factories, fixed feature points in a discharge voltage platform are usually used as a sorting basis with expert experience, causing poor accuracy in battery sorting. This study proposes a method based on feature point image adaptive recognition (FPIAR) and combined a semi-supervised fuzzy C-means (SFCM) clustering algorithm to achieve battery sorting. Accordingly, FPIAR obtained the multi-dimensional feature coordinate dataset by adaptively identifying the discharge voltage curve of each battery. The centre point of the accumulation of coordinates in the dataset was found by mean-shift algorithm and was used as the feature point of battery sorting voltage. In addition, the SFCM clustering algorithm was used to process the feature point of the sorting voltage to sort the batteries into groups. The simulation results and tests indicated that the SFCM clustering algorithm based on FPIAR processing can improve the accuracy of battery sorting.

Keywords: lithium battery; battery sorting; feature points; image adaptive recognition; SFCM clustering.

DOI: 10.1504/IJEHV.2022.127053

International Journal of Electric and Hybrid Vehicles, 2022 Vol.14 No.4, pp.314 - 334

Received: 25 Sep 2021
Accepted: 01 Nov 2021
Published online: 21 Nov 2022 *

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