基于产线大数据的锂离子电池一致性动态特性分选方法

doi:10.19799/j.cnki.2095-4239.2023.0819

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (4): 1188-1196.doi: 10.19799/j.cnki.2095-4239.2023.0819

• 电池智能制造、在线监测与原位分析专刊 • 上一篇下一篇

基于产线大数据的锂离子电池一致性动态特性分选方法

李革¹(), 孔祥栋², 孙跃东¹, 陈飞¹, 袁悦博³, 韩雪冰³(), 郑岳久¹

^1.上海理工大学机械学院，上海 200093
^2.四川赛鸥科技有限公司，四川宜宾 644000
^3.清华大学车辆与运载学院智能绿色车辆与交通全国重点实验室，北京 100084

收稿日期:2023-11-15 修回日期:2023-12-13 出版日期:2024-04-26 发布日期:2024-04-22
通讯作者: 韩雪冰 E-mail:leeger202@163.com;hanxuebing@mail.tsinghua.edu.cn
作者简介:李革（1998—），男，硕士研究生，研究方向为锂离子电池产线大数据，E-mail：leeger202@163.com；
基金资助:
国家自然科学基金项目(52277222);上海市自然科学基金项目(22ZR1444500)

Method for sorting the dynamic characteristics of lithium-ion battery consistency based on production line big data

Ge LI¹(), Xiangdong KONG², Yuedong SUN¹, Fei CHEN¹, Yuebo YUAN³, Xuebing HAN³(), Yuejiu ZHENG¹

^1.College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
^2.Sichuan Cell Technology Co. Ltd. , Yibin 644000, Sichuan, China
^3.School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China

Received:2023-11-15 Revised:2023-12-13 Online:2024-04-26 Published:2024-04-22
Contact: Xuebing HAN E-mail:leeger202@163.com;hanxuebing@mail.tsinghua.edu.cn

摘要/Abstract

摘要：

随着锂离子电池生产规模的迅速扩大，电池制造商急需高精度高效率的电池分选方法，增强电池成组后的一致性，从而提升电池组寿命、安全性和能量密度。基于容量和内阻等特性的传统分选技术可以满足成组后的静态一致性需求，但无法保证同组电池的动态一致性。因此，综合考虑电池在整个充放电过程中的性能，基于充放电电压曲线动态特性的分组方法是下一代分选技术的发展方向。本文基于电池产线大数据，从电池分容阶段的电压曲线提取关键动态特征，形成了基于K-means聚类的电池分选方法。此外，本文还从电池分容后的回充阶段提取了用于评估电池性能一致性的指标，并设计了一个以指标标准差为核心的电池一致性评价方法。与传统的电池分选方法相比较，本文方法分选后的电池综合性能一致性提高了15.65%。

关键词: 锂离子电池, 电池一致性, 电池分选, 聚类算法

Abstract:

As lithium-ion battery production rapidly expands, manufacturers urgently require high-precision and high-efficiency sorting methods to improve the consistency, lifespan, safety, and energy density of battery packs. Traditional techniques that rely on capacity and internal resistance address static consistency postgrouping but fail to ensure dynamic consistency within the same group. Addressing this, our study focuses on the dynamic characteristics of the charge-discharge voltage curve to propose a next-generation sorting approach. We extract key dynamic features from the voltage curve during the battery capacity grading process, utilizing big data from the production line, and employ K-means clustering for battery sorting. Furthermore, we assess battery performance consistency by analyzing metrics from the recharging stage postcapacity grading, devising an evaluation method based on the standard deviation of these metrics. Our proposed sorting method demonstrates a 15.65% improvement in the overall performance consistency of batteries compared to conventional approaches.

Key words: lithium-ion battery, battery consistency, battery sorting, clustering algorithm

中图分类号:

TM 912

李革, 孔祥栋, 孙跃东, 陈飞, 袁悦博, 韩雪冰, 郑岳久. 基于产线大数据的锂离子电池一致性动态特性分选方法[J]. 储能科学与技术, 2024, 13(4): 1188-1196.

Ge LI, Xiangdong KONG, Yuedong SUN, Fei CHEN, Yuebo YUAN, Xuebing HAN, Yuejiu ZHENG. Method for sorting the dynamic characteristics of lithium-ion battery consistency based on production line big data[J]. Energy Storage Science and Technology, 2024, 13(4): 1188-1196.

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一致性评价角度	评价指标	符号
dQ/dV曲线一致性	dQ/dV峰值	dQ/dV_peak
dQ/dV曲线一致性	dQ/dV峰值对应电压	V_d_Q_/d_V_peak
端电压一致性	充电起始电压	Volt_start
端电压一致性	充电末端电压	Volt_end
充电电量一致性	恒流与恒压充电电量比值	Rate_cc/cv
电压曲线一致性	恒流电压曲线平均距离	Distance_volt

分选方法	档位1		档位2		档位3		档位4
分选方法	平均值/Ah	标准差/Ah	平均值/Ah	标准差/Ah	平均值/Ah	标准差/Ah	平均值/Ah	标准差/Ah
随机分选	107.571	0.416	107.527	0.457	107.521	0.462	107.538	0.449
容量分选	106.214	0.180	107.142	0.238	107.833	0.258	108.704	0.164
动态分选	107.108	0.324	107.488	0.327	107.689	0.307	108.040	0.344

评价指标	容量分选	动态分选
dQ/dV峰值	0.8855	0.8158
dQ/dV峰值对应电压	0.7819	0.6797
充电起始电压	0.6815	0.5529
充电末端电压	0.7268	0.5621
恒流与恒压充电电量比值	0.6818	0.5886
电压曲线平均距离	0.7235	0.5804
综合性能一致性	0.7468	0.6299

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基于产线大数据的锂离子电池一致性动态特性分选方法

Method for sorting the dynamic characteristics of lithium-ion battery consistency based on production line big data

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