退役动力电池特性及分选方法研究

doi:doi:10.19912/j.0254-0096.tynxb.2024-0752

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退役动力电池特性及分选方法研究

更新时间：2025-10-22

- 退役动力电池特性及分选方法研究
- Vol. 46, Issue 9, Pages: 273-283(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0752
  CLC： TM912
- Published Online：14 October 2025，
  
  Published：2025
- 稿件说明：
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[J]. 2025, 46(9): 273-283.
DOI：

[J]. 2025, 46(9): 273-283. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0752.

摘要

针对退役锂电池梯次利用过程中单体间一致性较差、分选效率低、无法满足使用要求的问题

研究首先对退役锂电池进行容量、内阻、自放电特性、循环寿命特性及充放电温升性能检测

分析电池当前性能状态;然后利用主成分分析（PCA）法对充电容量、放电容量、库伦效率、充电能量、放电能量、开路电压及内阻这7项性能参数进行分选因子筛选;最后通过数据预处理、优化初始点选取方式、改进距离公式对K均值算法进行优化

实现对电池的聚类划分。研究结果表明

高倍率放电会加速电池老化

且低温环境下电池放电容量下降明显。利用PCA算法筛选得出选用电池放电容量、开路电压、内阻作为分选因子能较全面地反映电池性能;通过改进后的K均值算法对300组数据进行聚类分选得出

当K=4时聚类效果最优。

Abstract

Aiming at the problem of poor consistency between individual cells

low sorting efficiency and failure to meet the use requirements during the recycling of retired lithium batteries

the study first tested the capacity

internal resistance

self-discharge characteristics

cycle life characteristics and charge and discharge thermal behavior of waste lithium batteries to analyze the current status of the battery performance; then the principal component analysis （PCA） method was used to screen the sorting factors of seven performance parameters

including charging capacity

discharge capacity

coulomb efficiency

charging energy

discharge energy

open circuit voltage and internal resistance; finally

the -means algorithm was optimized by preprocessing the data

optimizing the initial point selection method and improving the distance formula to achieve clustering of the battery. The results show that high-rate discharge will accelerate battery aging

and the battery discharge capacity will decrease significantly under lower temperature conditions. Through PCA algorithm analysis

the battery discharge capacity

open circuit voltage and internal resistance can be selected as sorting factors

fully reflecting battery performance. The improved -means algorithm was used to cluster and sort 300 groups of data

and it was found that when =4

the clustering effect was optimal.

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references

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