Research on lithium battery sorting method based on image adaptive recognition of feature points. (15th November 2022)

• Record Type: Journal Article
• Authors: Zhou, Yongqin; Wang, Yubin; Chang, Yujia; Huang, Ce; Li, Ran
• Is Part Of: International journal of electric and hybrid vehicles. Volume 14:Number 4(2022)
• Page Start: 314
• Page End: 334
• Publication Date: 2022-11-15
• Subjects: lithium battery -- battery sorting -- feature points -- image adaptive recognition -- SFCM clustering; Electric vehicles -- Periodicals; 629.229305
• Languages: English
• ISSNs: 1751-4088

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.