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

doi:10.11930/j.issn.1004-9649.202401040

Abstract

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

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.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401040

https://www.electricpower.com.cn/EN/Y2024/V57/I6/9

Figures/Tables 21

References 26

1	任大伟, 侯金鸣, 肖晋宇, 等. 支撑双碳目标的新型储能发展潜力及路径研究[J]. 中国电力, 2023, 56 (8): 17- 25.
	REN Dawei, HOU Jinming, XIAO Jinyu, et al. Research on development potential and path of new energy storage supporting carbon peak and carbon neutrality[J]. Electric Power, 2023, 56 (8): 17- 25.
2	胡静, 黄碧斌, 孟子涵, 等. 电网功能替代性储能替代效果评估和政策机制研究[J]. 中国电力, 2023, 56 (8): 10- 16.
	HU Jing, HUANG Bibin, MENG Zihan, et al. Research on alternative effect evaluation and policy mechanisms of grid-side functional alternative energy storage[J]. Electric Power, 2023, 56 (8): 10- 16.
3	LV Y, GENG X, LUO W, et al. Review on influence factors and prevention control technologies of lithium-ion battery energy storage safety[J]. Journal of Energy Storage, 2023, 72, 108389. DOI
4	LYU P Z, LIU X J, QU J, et al. Recent advances of thermal safety of lithium ion battery for energy storage[J]. Energy Storage Materials, 2020, 31, 195- 220. DOI
5	XIONG R, SUN W Z, YU Q Q, et al. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles[J]. Applied Energy, 2020, 279, 115855. DOI
6	YU H Q, LI J F, JI Y K, et al. Life-cycle parameter identification method of an electrochemical model for lithium-ion battery pack[J]. Journal of Energy Storage, 2022, 47, 103591. DOI
7	颜湘武, 邓浩然, 郭琪, 等. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34 (18): 3937- 3948.
	YAN Xiangwu, DENG Haoran, GUO Qi, et al. Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization[J]. Transactions of China Electrotechnical Society, 2019, 34 (18): 3937- 3948.
8	张媛, 夏向阳, 岳家辉, 等. 基于电池簇放电电量的电池堆不一致性在线监测方法[J]. 中国电力, 2023, 56 (7): 207- 215, 227.
	ZHANG Yuan, XIA Xiangyang, YUE Jiahui, et al. Online monitoring method of battery stack inconsistency based on discharge quantity of battery clusters[J]. Electric Power, 2023, 56 (7): 207- 215, 227.
9	NAGUIB M, KOLLMEYER P, EMADI A. Lithium-ion battery pack robust state of charge estimation, cell inconsistency, and balancing: review[J]. IEEE Access, 2021, 9, 50570- 50582. DOI
10	HAN Y J, YUAN H Y, LI J, et al. Study on influencing factors of consistency in manufacturing process of vehicle lithium-ion battery basedon correlation coefficient and multivariate linear regression model[J]. Advanced Theory and Simulations, 2021, 4(8): 202100070.
11	LI D, ZHANG Z, LIU P, et al. DBSCAN-based thermal run away diagnosis of battery systems for electric vehicles[J]. Energies, 2019, 12 (15): 2977. DOI
12	张朝龙, 赵筛筛, 何怡刚. 基于信息熵与PSO-LSTM的锂电池组健康状态估计方法[J]. 机械工程学报, 2022, 58 (10): 180- 190.
	ZHANG Chaolong, ZHAO Shaishai, HE Yigang. State-of-health estimate for lithium-ion battery using information entropy and PSO-LSTM[J]. Journal of Mechanical Engineering, 2022, 58 (10): 180- 190.
13	ZHAO Y, LIU P, WANG Z P, et al. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods[J]. Applied Energy, 2017, 207, 354- 362. DOI
14	FENG X S, ZHANG X K, XIANG Y. An inconsistency assessment method for backup battery packs based on time-series clustering[J]. Journal of Energy Storage, 2020, 31, 101666. DOI
15	TIAN J Q, LIU X H, CHEN C B, et al. Feature fusion-based inconsistency evaluation for battery pack: improved Gaussian mixture model[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24 (1): 446- 458. DOI
16	LIN M, XIE H N, SHAN M Y. A hybrid multiscale permutation entropy-based fault diagnosis and inconsistency evaluation approach for lithium battery of E-vehicles[J]. IEEE Access, 1023, 10, 104757- 104768.
17	FANG W D, CHEN H L, ZHOU F M. Fault diagnosis for cell voltage inconsistency of a battery pack in electric vehicles based on real-world driving data[J]. Computers and Electrical Engineering, 2022, 102, 108095. DOI
18	CUI Z R, CUI N X, RAO J, et al. Current distribution estimation of parallel-connected batteries for inconsistency diagnosis using long short-term memory networks[J]. IEEE Transactions on Transportation Electrification, 2022, 8 (1): 1013- 1025. DOI
19	何洪文, 王浩宇, 王勇, 等. 基于实车运行数据的锂离子电池健康状态估计[J]. 机械工程学报, 2023, 59 (22): 46- 58. DOI
	HE Hongwen, WANG Haoyu, WANG Yong, et al. Lithium-ion battery state of health estimation based on real-world driving data[J]. Journal of Mechanical Engineering, 2023, 59 (22): 46- 58. DOI
20	VICHARD L, RAVEY A, VENET P, et al. A method to estimate battery SOH indicators based on vehicle operating data only[J]. Energy, 2021, 225, 120235. DOI
21	SHEN J W, MA W S, SHU X, et al. Accurate state of health estimation for lithium-ion batteries under random charging scenarios[J]. Energy, 2023, 279, 128092. DOI
22	王志强, 孙刚. 中国金融发展规模、结构、效率与经济增长关系的经验分析[J]. 管理世界, 2003, (07): 13- 20.
	WANG Zhiqiang, SUN Gang. Empirical analysis of the relationship between China's financial development scale, structure, efficiency and economic growth[J]. Management World, 2003, (07): 13- 20.
23	李龙飞, 钟海旺, 张广伦, 等. 基于因果分析的行业月度电量预测方法[J]. 电网技术, 2023, 47 (9): 3629- 3637.
	LI Longfei, ZHONG Haiwang, ZHANG Guanglun, et al. Monthly sectoral electricity demand prediction based on causal analysis[J]. Power System Technology, 2023, 47 (9): 3629- 3637.
24	殷涛, 薛阳, 杨艺宁, 等. 基于向量自回归模型的高损线路窃电检测[J]. 中国电机工程学报, 2022, 42 (3): 1015- 1023.
	YIN Tao, XUE Yang, YANG Yining, et al. Electricity theft detection of high-loss line with vector autoregression[J]. Proceedings of the CSEE, 2022, 42 (3): 1015- 1023.
25	苏连成, 朱娇娇, 郭高鑫, 等. 基于XGBoost和Wasserstein距离的风电机组塔架振动监测研究[J]. 太阳能学报, 2023, 44 (1): 306- 312.
	SU Liancheng, ZHU Jiaojiao, GUO Gaoxin, et al. Research on wind turbing tower vibration monitoring based on XGBoost and Wasserstein distance[J]. Acta Energiae Solaris Sinica, 2023, 44 (1): 306- 312.
26	齐火箭, 张新瑞, 王嘉宏, 等. 动力锂离子电池组均衡策略研究[J/OL]. 电源学报: 1–17[2024-04-08]. http://kns.cnki.net/kcms/detail/12.1420.TM.20220215.0853.002.html.
	QI Huojian, ZHANG Xinrui, WANG Jiahong, et al. Research on equilibrium strategy of power lithium-ion battery pack[J/OL]. Journal of Power Supply, 1–17[2024-04-08]. http://kns.cnki.net/kcms/detail/12.1420.TM.20220215.0853.002.html.

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

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

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

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

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