基于向量误差修正模型的电池簇不一致检测方法及智能运维方案

doi:10.11930/j.issn.1004-9649.202401040

中国电力 ›› 2024, Vol. 57 ›› Issue (6): 9-17, 44.DOI: 10.11930/j.issn.1004-9649.202401040

• 储能用锂离子电池本体安全关键技术 • 上一篇下一篇

基于向量误差修正模型的电池簇不一致检测方法及智能运维方案

郭源¹(), 夏向阳¹(), 岳家辉¹(), 李辉², 吴晋波²

1. 长沙理工大学电气与信息工程学院，湖南长沙　410114
2. 国网湖南省电力科学研究院，湖南长沙　410000

收稿日期:2024-01-09 出版日期:2024-06-28 发布日期:2024-06-25
作者简介:郭源（1999—），男，硕士研究生，从事储能安全与数值分析研究，E-mail：1574072953@qq.com
夏向阳（1968—），男，通信作者，教授，博士生导师，从事柔性直流输电控制和储能安全控制技术研究，E-mail：307351045@qq.com
岳家辉（1994—），男，博士研究生，从事储能电站安全监测、运行与控制等研究，E-mail：243952600@qq.com
基金资助:
国家自然科学基金资助项目（柔性直流输电交流侧故障下换流器多桥臂主动应对的能量调控机理及穿越控制研究，51977014）。

Battery Cluster Inconsistency Detection Method and Intelligent O&M Scheme Based on Vector Error Correction Model

Yuan GUO¹(), Xiangyang XIA¹(), Jiahui YUE¹(), Hui LI², Jinbo WU²

1. School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
2. State Grid Hunan Electric Power Research Institute, Changsha 410000, China

Received:2024-01-09 Online:2024-06-28 Published:2024-06-25
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Energy Regulation Mechanism and Fault Ride-Through Control of Multi-armed Converter of AC Side Fault in MMC-HVDC System, No.51977014).

摘要/Abstract

摘要：

针对储能电站实际运行数据中存在电池数据不完整、数据片段化导致检测不准确的问题，提出基于向量误差修正模型的电池簇不一致检测方法。该方法根据随机电压片段数据构建电池簇与电池单体的向量误差修正模型，计算脉冲响应函数，分析电池单体对电池簇的动态作用机制，判断电池簇不一致程度，再通过方差分解分析确定异常电池单体及后续运维。最后，根据储能电站实际运行数据进行分析，验证了电池簇不一致检测方法及运维方案的可行性和有效性，并在100 kW/200 kW·h储能平台进行实际工程测试。

关键词: 电池簇不一致, 随机片段数据, 向量误差修正模型, 智能运维方案

Abstract:

To solve the problems of incomplete battery data and fragmented data segments leading to inaccurate detection in the actual operation data of energy storage power stations, this paper proposes a battery cluster inconsistency detection method based on the vector error correction model. In this method, the vector error correction model of battery clusters and battery cells is constructed using random voltage fragment data, the impulse response function is calculated, the dynamic mechanism of battery cells on battery clusters is analyzed, and the inconsistency degree of battery clusters is assessed. Subsequently, the abnormal battery cells and subsequent operation and maintenance are identified by variance decomposition analysis. Finally, through the actual operation data of the energy storage power station, the feasibility and effectiveness of the battery cluster inconsistency detection method and operation and maintenance scheme are verified in an actual engineering test on a 100 kW/200 kW·h energy storage platform.

Key words: battery inconsistency, random fragment data, vector error correction model, intelligent O&M scheme

郭源, 夏向阳, 岳家辉, 李辉, 吴晋波. 基于向量误差修正模型的电池簇不一致检测方法及智能运维方案[J]. 中国电力, 2024, 57(6): 9-17, 44.

Yuan GUO, Xiangyang XIA, Jiahui YUE, Hui LI, Jinbo WU. Battery Cluster Inconsistency Detection Method and Intelligent O&M Scheme Based on Vector Error Correction Model[J]. Electric Power, 2024, 57(6): 9-17, 44.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202401040

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图/表 21

图 1 总体流程示意

Fig.1 Overall flow

图 2 电池单体的脉冲响应分布

Fig.2 Impulse response distribution of battery cells

表 1 标准正态分布

Table 1 Standard normal distribution

数值分布		概率
$ [\mathrm{\mu }+\mathrm{\sigma }，\mathrm{\mu }+2\mathrm{\sigma }] $		0.1359
$ [\mathrm{\mu }+0.5\mathrm{\sigma }，\mathrm{\mu }+\mathrm{\sigma }] $		0.1498
$ [\mathrm{\mu }-0.5\mathrm{\sigma }，\mathrm{\mu }+0.5\mathrm{\sigma }] $		0.3830
$ [\mathrm{\mu }-\mathrm{\sigma }，\mathrm{\mu }-0.5\mathrm{\sigma }] $		0.1498
$ [\mathrm{\mu }-2\mathrm{\sigma }，\mathrm{\mu }-\mathrm{\sigma }] $		0.1359

图 3 各电池单体方差分解情况

Fig.3 Cell variance decomposition in battery pool

表 2 智慧运维方案

Table 2 Intelligent O&M scheme

电池单体状态		处理措施
聚类4		正常监测
不一致情况正常，聚类1和聚类2		离线均衡
不一致情况接近阈值，聚类2
聚类3
不一致情况超过阈值，聚类1和聚类2		进行更换
不一致情况接近阈值，聚类1		进行更换

图 4 湖南省某锂离子电池储能电站设备

Fig.4 Lithium-ion battery energy storage power station in Hunan Province

表 3 最优滞后阶数确定

Table 3 Optimal hysteresis determination

滞后期	L_P	E_FP	C_IA	C_S	H_Q
0	—	1.80×10⁷	–1.3414	–1.0935	–1.2830
1	28001.20	1.85×10⁷	–1.3852	0.1025	–1.0347
2	3565.87	5.07×10⁷	–0.7983	1.9292	–0.1557
3	1997.88	2.62×10^{8 1)}	–5.2238¹⁾	–1.2564¹⁾	–4.2892¹⁾
4	1901.61¹⁾	1.80×10⁷	–1.3414	–1.0935	–1.2830

表 4 协整检验结果

Table 4 Cointegration test results

原假设	特征值	迹统计量	5%水平临界值	概率
R=0¹⁾	0.988996	161.932000	69.81889	0.0000
R≤1¹⁾	0.747461	62.723210	47.85613	0.0011
R≤2¹⁾	0.620352	32.447030	29.79707	0.0242
R≤3¹⁾	0.318289	11.139810	15.49471	0.2031
R≤4	0.115918	2.710526	3.841466	0.0997

图 5 电池单体脉冲响应情况

Fig.5 Cell impulse response

表 5 各聚类情况的成员数量

Table 5 Number of members in each cluster

聚类情况	数量	比例/%
聚类1	31	12.92
聚类2	74	30.83
聚类3	68	28.34
聚类4	49	20.42
聚类5	18	7.50

图 6 脉冲响应各聚类中心特征

Fig.6 Impulse response to characteristics of each cluster center

图 7 方差分解各聚类中心特征

Fig.7 Variance decomposes characteristics of each cluster center

图 8 22 S1 P电池簇模型

Fig.8 22 S1 P battery pack model

图 9 非均衡状态下各电池单体电压曲线

Fig.9 Voltage curves of each cell in a non-equilibrium state

图 10 电池差异度为2时电池单体脉冲响应情况

Fig.10 Impulse response of battery cell with a cell difference of 2

表 6 不同电池差异度的相似度

Table 6 Similarity at different battery differences

电池差异度	相似度	电池差异度	相似度
0.5	0.0435	10.0	0.2333
1.0	0.0656	15.0	0.2892
2.0	0.1050	20.0	0.3673
5.0	0.1454

图 11 电池差异度为2时电池单体方差分解情况

Fig.11 Variance decomposition of battery cell with a battery difference of 2

表 7 不同电池差异度更换电池后的相似度

Table 7 Similarity after replacing battery at different battery differences

电池差异度	更换电池前相似度	更换电池后相似度
0.5	0.0435	0.0474
1.0	0.0656	0.0752
2.0	0.1050	0.0840
5.0	0.1454	0.0936
10.0	0.2333	0.0867
15.0	0.2892	0.0812
20.0	0.3673	0.0795

表 8 电池单体监测等级

Table 8 Battery monitoring levels

电池颜色	电池单体状态	处理措施
绿色	聚类4	正常监测
黄色	不一致情况正常，聚类1和聚类2	离线均衡
	不一致情况接近阈值，聚类2
	聚类3
红色	不一致情况超过阈值，聚类1和聚类2	进行更换
红色	不一致情况接近阈值，聚类1	进行更换

图 12 储能系统平台与检测系统界面

Fig.12 Energy storage system platform and interface of measurement system

图 13 储能平台电池单体脉冲响应情况

Fig.13 Impulse response of battery cells on energy storage platform

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基于向量误差修正模型的电池簇不一致检测方法及智能运维方案

Battery Cluster Inconsistency Detection Method and Intelligent O&M Scheme Based on Vector Error Correction Model

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基于向量误差修正模型的电池簇不一致检测方法及智能运维方案

Battery Cluster Inconsistency Detection Method and Intelligent O&M Scheme Based on Vector Error Correction Model

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