基于运行数据的储能电站电池组一致性评估方法

doi:10.19799/j.cnki.2095-4239.2023.0332

摘要/Abstract

摘要：

本工作以大容量磷酸铁锂电池储能电站为研究对象，立足于储能系统中处于工程场景的电池组日常运行数据。首先，根据电池运行数据分析表征锂离子电池电压、温度的一致性关键参量；其次，提取能够有效反映电池组一致性的评估特征；最后，基于此类特征，将储能电站的一致性分析分为两个层次，提出了针对站内大规模电池组的一致性分析算法以及基于DBSCAN (density-based spatial clustering of applications with noise) 聚类的电池组内异常单体电池筛选算法。结果表明，所提算法能够高效地获取储能电站运行数据中与电池组一致性相关的关键电气特征量，准确判断储能系统内电池组的一致性情况以及定位可能出现故障的单体电池。本研究有助于判断在工程场景中大规模电池组的一致性状态，并能及时准确地筛选出异常单体电池，保障储能电站电池组的安全性。

关键词: 储能电站, 锂离子电池, DBSCAN聚类算法, 一致性评估

Abstract:

This study takes a large-capacity power station of lithium iron phosphate battery energy storage as the research object, based on the daily operation data of battery packs in the engineering scene of energy storage systems. First, the key parameters characterizing the voltage and temperature consistency of Li-ion batteries were analyzed according to the operating data of the battery. Second, the evaluation features that can effectively reflect the battery pack consistency were extracted. Finally, based on such characteristics, the consistency analysis of the energy storage power station was divided into two levels, and the consistency analysis algorithm was proposed for large-scale battery packs in the station. Furthermore, a screening algorithm was proposed for abnormal cells in battery packs based on density-based spatial clustering of applications with noise (DBSCAN) clustering. The results showed that the proposed algorithm could efficiently obtain the key electrical characteristics related to the battery pack consistency in the operation data of the energy storage power station. Moreover, it could accurately judge the battery pack consistency in the energy storage system and locate the single battery that may fail. This study is helpful in judging the consistent state of large-scale battery packs in engineering scenarios. It can also timely and accurately screen out abnormal single batteries to ensure the battery packs' safety in energy storage power stations.

Key words: energy storage power station, lithium-ion batteries, DBSCAN clustering algorithm, consistency evaluation

中图分类号:

TM 911.3

高欣, 王若谷, 高文菁, 邓泽军, 梁睿祺, 杨騉. 基于运行数据的储能电站电池组一致性评估方法[J]. 储能科学与技术, 2023, 12(9): 2937-2945.

Xin GAO, Ruogu WANG, Wenjing GAO, Zejun DENG, Ruiqi LIANG, Kun YANG. Consistency evaluation method of battery pack in energy storage power station based on running data[J]. Energy Storage Science and Technology, 2023, 12(9): 2937-2945.

图/表 8

图1

表1

电压特征量"

特征量名称	特征量得到方式
$m a x V m d i f f$	$m a x V m d i f f = m a x (V m d i f f)$
$a v g V m d i f f$	$a v g V m d i f f = m e a n (V m d i f f)$
$a v g V m m a x$	$a v g V m m a x = m e a n (V m m a x)$
$a v g V E n m$	$a v g V E n m = m e a n (V E n m)$
$m a x V E n m$	$m a x V E n m = m a x (V E n m)$
$v a r V E n m$	$v a r V E n m = v a r (V E n m)$

表1

表2

温度特征量"

特征量名称	特征量得到方式
$m a x T m d i f f$	$m a x T m d i f f = m a x (T m d i f f)$
$a v g T m d i f f$	$a v g T m d i f f = m e a n (T m d i f f)$
$a v g T m m a x$	$a v g T m m a x = m e a n (T m m a x)$
$a v g T E n m$	$a v g T E n m = m e a n (T E n m)$
$m a x T E n m$	$m a x T E n m = m a x (T E n m)$
$v a r T E n m$	$v a r T E n m = v a r (T E n m)$

表2

图2

图3

图4

图5

图6

参考文献 20

1	武昭原, 周明, 王剑晓, 等. 双碳目标下提升电力系统灵活性的市场机制综述[J]. 中国电机工程学报, 2022, 42(21): 7746-7763.
	WU Z Y, ZHOU M, WANG J X, et al. Review on market mechanism to enhance the flexibility of power system under the dual-carbon target[J]. Proceedings of the Chinese Society for Electrical Engineering, 2022, 42(21): 7746-7763.
2	王宁, 陈志强, 刘明义, 等. 基于模糊综合评价的锂离子电池健康状态评估[J]. 发电技术, 2022, 43(5): 784-791.
	WANG N, CHEN Z Q, LIU M Y, et al. Health status assessment of lithium-ion battery based on fuzzy comprehensive evaluation[J]. Power Generation Technology, 2022, 43(5): 784-791.
3	黄燕琴, 聂金泉, 王敖, 等. 锂离子电池不一致性综述[J]. 时代汽车, 2022(5): 102-107.
	HUANG Y Q, NIE J Q, WANG A, et al. Review on inconsistent of lithium-ion batteries[J]. Auto Time, 2022(5): 102-107.
4	王琳舒. 锂离子动力电池一致性分析模型及其影响因素的仿真分析[D]. 北京: 北京有色金属研究总院, 2020.
	WANG L S. Consistency analysis model of lithium-ion power battery and simulation analysis of its influencing factors[D]. Beijing: General Research Institute for Nonferrous Metals, 2020.
5	SEQUINO L, VAGLIECO B M. Potential of infrared temperature measurements for the online estimation of the state-of-charge of a Li-polymer battery[J]. Journal of Energy Storage, 2021, 44: 103532.
6	杨胜杰, 罗冰洋, 王菁, 等. 基于容量增量曲线峰值区间特征参数的锂离子电池健康状态估算[J]. 电工技术学报, 2021, 36(11): 2277-2287.
	YANG S J, LUO B Y, WANG J, et al. State of health estimation for lithium-ion batteries based on peak region feature parameters of incremental capacity curve[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2277-2287.
7	刘宇龄, 孟锦豪, 彭乔, 等. 基于NSGA-Ⅱ遗传算法的锂电池均衡指标优化[J]. 储能科学与技术, 2023, 12(6): 1946-1956.
	LIU Y L, MENG J H, PENG Q, et al. NSGA-Ⅱ genetic algorithm-based optimization of the lithium battery equalization index[J]. Energy Storage Science and Technology, 2023, 12(6): 1946-1956.
8	黄保帅, 张巍. 基于单体一致性对动力锂电池性能的影响研究[J]. 电源技术, 2018, 42(9): 1310-1311, 1320.
	HUANG B S, ZHANG W. Influence of cell consistency on performance of power lithium battery[J]. Chinese Journal of Power Sources, 2018, 42(9): 1310-1311, 1320.
9	TIAN J Q, WANG Y J, LIU C, et al. Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles[J]. Energy, 2020, 194: 116944.
10	魏刚. 基于阻抗谱的锂离子电池快速状态评估系统设计[D]. 哈尔滨: 哈尔滨工业大学, 2020.
	WEI G. Design of rapid state evaluation system for lithium-ion battery based on impedance spectrum[D]. Harbin: Harbin Institute of Technology, 2020.
11	靳文涛, 李煜阳, 贾学翠, 等. 储能电站电池一致性综合评估方法研究[J]. 电器与能效管理技术, 2022(5): 23-28.
	JIN W T, LI Y Y, JIA X C, et al. Research on comprehensive evaluation method of battery consistency in energy storage power station[J]. Electrical & Energy Management Technology, 2022(5): 23-28.
12	王帅, 尹忠东, 郑重, 等. 电池模组一致性影响因素在放电电压曲线簇上的表征[J]. 电工技术学报, 2020, 35(8): 1836-1847.
	WANG S, YIN Z D, ZHENG Z, et al. Representation of influence factors for battery module consistency on discharge voltage curves[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1836-1847.
13	程功. 电池组一致性的统计特性与变化规律研究[D]. 北京: 北京交通大学, 2017.
	CHENG G. Study on statistical characteristics and changing rules of consistency of battery pack[D]. Beijing: Beijing Jiaotong University, 2017.
14	潘岳, 韩雪冰, 欧阳明高, 等. 锂离子电池内短路检测算法及其在实际数据中的应用[J]. 储能科学与技术, 2023, 12(1): 198-208.
	PAN Y, HAN X B, OUYANG M G, et al. Research on the detection algorithm for internal short circuits in lithium-ion batteries and its application to real operating data[J]. Energy Storage Science and Technology, 2023, 12(1): 198-208.
15	曾建邦, 张月娅, 张壮, 等. 基于动态k值K-means++聚类的电动汽车动力电池电压不一致故障识别方法[J]. 中国科学(技术科学), 2023, 53(1): 28-40.
	ZENG J B, ZHANG Y Y, ZHANG Z, et al. Identification of power battery voltage inconsistency faults in electric vehicles based on K-means++ clustering with dynamic k-values[J]. Scientia Sinica (Technologica), 2023, 53(1): 28-40.
16	程阳阳. 一种基于改进型K-means聚类算法的退役电池组筛选重组方法[J]. 能源工程, 2022, 42(4): 36-42, 55.
	CHENG Y Y. An improved K-means clustering algorithm based screening and recombination method of retired battery pack[J]. Energy Engineering, 2022, 42(4): 36-42, 55.
17	贾俊, 胡晓松, 邓忠伟, 等. 数据驱动的锂离子电池健康状态综合评分及异常电池筛选[J]. 机械工程学报, 2021, 57(14): 141-149, 159.
	JIA J, HU X S, DENG Z W, et al. Data-driven comprehensive evaluation of lithium-ion battery state of health and abnormal battery screening[J]. Journal of Mechanical Engineering, 2021, 57(14): 141-149, 159.
18	LIN D, LI Z H, FENG Y B, et al. Research on inconsistency identification of lithium-ion battery pack based on operational data[C]//2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). May 10-12, 2021, Victoria, BC, Canada. IEEE, 2021: 498-502.
19	郭亚军. 综合评价理论、方法及应用[M]. 北京: 科学出版社, 2007.
	GUO Y J. Theory, method and application of comprehensive evaluation[M]. Beijing: Science Press, 2007.
20	SANDER J, ESTER M, KRIEGEL H P, et al. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 169-194.

[1]	李悦, 王博, 吴楠. 石墨烯/Si/SiO _x 纳米复合材料的制备及储锂性能研究[J]. 储能科学与技术, 2023, 12(9): 2752-2759.
[2]	曾少鸿, 吴伟雄, 刘吉臻, 汪双凤, 叶石丰, 冯振宇. 锂离子电池浸没式冷却技术研究综述[J]. 储能科学与技术, 2023, 12(9): 2888-2903.
[3]	陈欣, 李云伍, 梁新成, 李法霖, 张志冬. 基于模态分解的Transformer-GRU联合电池健康状态估计[J]. 储能科学与技术, 2023, 12(9): 2927-2936.
[4]	李纪伟, 刘睿涵, 吕桃林, 潘隆, 马常军, 李清波, 赵芝芸, 杨文, 解晶莹. 基于局部离群点检测和标准差方法的锂离子电池组早期故障诊断[J]. 储能科学与技术, 2023, 12(9): 2917-2926.
[5]	黄晓伟, 李少鹏, 张校刚. 负极补锂锂化裕度对电芯性能的影响及机理研究[J]. 储能科学与技术, 2023, 12(9): 2727-2734.
[6]	周向阳, 胡颖杰, 梁家浩, 周其杰, 文康, 陈松, 杨娟, 唐晶晶. 天然鳞片石墨球化尾料的高性能负极材料制备及储锂特性研究[J]. 储能科学与技术, 2023, 12(9): 2767-2777.
[7]	江婉薇, 梁呈景, 钱历, 刘梅城, 朱孟想, 马骏. 锡基三维石墨烯泡沫调控及其锂电池负极性能[J]. 储能科学与技术, 2023, 12(9): 2746-2751.
[8]	官亦标, 沈进冉, 刘家亮, 渠展展, 高飞, 刘施阳, 郭翠静, 周淑琴, 付珊珊. 以安全高质量应用为导向的储能锂离子电池综合性能评价标准[J]. 储能科学与技术, 2023, 12(9): 2946-2953.
[9]	郭煜, 王亦伟, 钟隽, 杜进桥, 田杰, 李艳, 蒋方明. 基于增量容量曲线的锂离子电池微内短路故障诊断方法[J]. 储能科学与技术, 2023, 12(8): 2536-2546.
[10]	唐程波, 锁要红, 何昭坤. 基于正弦函数的液冷板上流体流向对锂离子电池散热性能的影响[J]. 储能科学与技术, 2023, 12(8): 2547-2555.
[11]	左安昊, 方儒卿, 李哲. 锂离子电池单颗粒动力学表征方法综述[J]. 储能科学与技术, 2023, 12(8): 2457-2481.
[12]	陈满, 程志翔, 赵春朋, 彭鹏, 雷旗开, 金凯强, 王青松. 锂离子电池储能集装箱爆炸危害数值模拟[J]. 储能科学与技术, 2023, 12(8): 2594-2605.
[13]	黄永浩, 臧国景, 朱霨亚, 廖友好, 李伟善. LiF添加剂改善含锂陶瓷隔膜与4.35 V LiNi_0.8Co_0.1Mn_0.1O₂ 正极的界面稳定性[J]. 储能科学与技术, 2023, 12(8): 2361-2369.
[14]	杨佳兴, 张恒运, 徐屹东. 基于电化学-热耦合模型的锂离子电池组件产热分析[J]. 储能科学与技术, 2023, 12(8): 2615-2625.
[15]	张吉禄, 董育辰, 宋强, 袁思鸣, 郭孝东. 多晶及单晶高镍三元材料LiNi_0.9Co_0.05Mn_0.05O₂ 的可控制备及其电化学储锂特性[J]. 储能科学与技术, 2023, 12(8): 2382-2389.