首页|基于电池容量衰减特征的锂电池跳水点和衰减程度识别方法

基于电池容量衰减特征的锂电池跳水点和衰减程度识别方法

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锂电池容量衰减程度及跳水点的估计是电池梯次利用与回收领域的重要问题之一。针对锂电池容量衰减程度难以直接度量和人工判断效果较差的问题,本文提出了一种基于电池容量衰减特征的锂电池跳水点和衰减程度识别方法。首先计算锂电池的容量衰减特征,然后应用Savitzky-Golay滤波算法来修正异常电池容量数据,并结合3σ准则得出衰减特征和衰减量比例。随后使用K均值聚类算法以找到衰减阈值,最后使用滑动观察窗口法来确定电池容量跳水点和衰减程度。实验表明,该方法具有良好的判断效果,可以应用于锂电池衰减现象识别。
Identification Method for Lithium Battery Diving Point and Attenuation Degree Based on Battery Capacity Attenuation Characteristics
The estimation of the degree of capacity decay and drop-off point of lithium-ion batteries is one of the impor-tant issues in the field of battery recycling and reuse.Due to difficulties in directly measuring the capacity decay and poor ef-fectiveness of manual judgment,this paper proposes a lithium-ion battery drop-off point and decay identification method based on the capacity decay characteristics of batteries.Firstly,the capacity decay characteristics of lithium-ion batteries are calculated,and then the Savitzky-Golay filter algorithm is applied to correct abnormal battery capacity data,combined with the 3σ rule to determine the decay characteristics and decay proportion.Subsequently,the K-means clustering algorithm is used to find the decay threshold,and finally,the sliding observation window method is used to determine the battery capacity drop-off point and decay degree.The experiment shows that this method has good judgment effectiveness and can be used in the field of lithium-ion battery decay phenomenon identification.

Lithium batterydiving pointsattenuation characteristicsSavitzky-Golay filteringK-means clustering

李彦梅、涂亮、张朝龙

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安庆师范大学 电子工程与智能制造学院,安徽 安庆 246133

金陵科技学院 智能科学与控制工程学院,江苏 南京 211169

锂电池 跳水点 衰减特征 Savitzky-Golay滤波 K均值聚类

安徽省高等学校科学研究重点项目省级研究生创新创业项目

2022AH0510432022cxcysj161

2024

安庆师范大学学报(自然科学版)
安庆师范学院

安庆师范大学学报(自然科学版)

影响因子:0.252
ISSN:1007-4260
年,卷(期):2024.30(3)
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