基于动态时间规整和多维标度策略的串联锂离子电池组异常电池可视化识别方法

doi:10.12028/j.issn.2095-4239.2018.0193

储能科学与技术 ›› 2019, Vol. 8 ›› Issue (1): 180-190.doi: 10.12028/j.issn.2095-4239.2018.0193

基于动态时间规整和多维标度策略的串联锂离子电池组异常电池可视化识别方法

钟国彬¹, 沈佳妮², 徐凯琪¹, 王乾坤², 贺益君², 苏伟³, 马紫峰²

1 广东电网有限责任公司电力科学研究院, 广东广州 510080;
2 上海交通大学化学工程系, 上海 200240;
3 广东电科院能源技术有限公司, 广东广州 510080

收稿日期:2018-09-05 修回日期:2018-09-28 出版日期:2019-01-01 发布日期:2018-10-25
通讯作者: 贺益君,副研究员,主要研究方向为能量储存与转换及电池管理系统,E-mail:heyijun@sjtu.edu.cn。
作者简介:钟国彬(1984-),男,博士,主要研究方向为化学储能技术及储能在电网的应用,E-mail:zhongguobin001@163.com
基金资助:
南方电网公司科技项目（GDKJXM00000039）。

Dynamic time warping and multidimensional scaling approach based abnormal battery visual recognition for series-connected lithium-ion batteries pack

ZHONG Guobin¹, SHEN Jiani², XU Kaiqi¹, WANG Qiankun², HE Yijun², SU Wei³, MA Zifeng²

1 Electric Power Research Institute of Guangdong Power Grid Co. Ltd., Guangzhou 510080, Guangdong, China;
2 Department of Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
3 Guangdong Diankeyuan Energy Technology Co. Ltd., Guangzhou 510080, Guangdong, China

Received:2018-09-05 Revised:2018-09-28 Online:2019-01-01 Published:2018-10-25

摘要/Abstract

摘要： 精确、可靠地识别异常电池是保障电池系统安全、稳定运行的有效手段。但是，从实时测量得到的电流、电压和温度有限外部信息，推断内阻、容量等电池内部信息，并识别异常电池难度很大。本工作针对串联锂离子电池组，基于各单体电压测量数据，提出了一种融合动态时间规整和多维标度策略的异常电池识别方法。通过采用动态时间规整策略，计算动态时间规整距离相似性指标，以消除电池组中荷电状态不一致的影响；进而结合多维标度法提取异常特征参数，实现异常电池可视化识别。通过电池系统仿真实验，验证了所提方法的有效性，为异常电池在线识别提供了一种有效技术。

关键词: 锂离子电池组, 动态时间规整, 多维标度, 异常电池识别

Abstract: Accurate and reliable recognition of abnormal batteries is of vital to ensure the stable and safe operation of the battery system. However, it is difficult to deduce the internal information of the battery such as internal resistance and capacity from the limited external information of real-time measured current, voltage and temperature, and consequently to identify abnormal cells. In this paper, based on the voltage measurement of each cell in series lithium-ion batteries pack, an abnormal battery recognition method is proposed, in which dynamic time warping and multi-dimensional scaling strategy are properly combined. The dynamic time warping strategy is used to calculate the dynamic time warping distance to eliminate the effect of inconsistent state of charge in the battery pack, and then the multi-dimensional scaling method is used to extract the abnormal features to achieve visual recognition of abnormal batteries. The effectiveness of the proposed method is demonstrated by battery system simulation results. The results show that the proposed method is a potential promising technology for on-line recognition of abnormal batteries.

Key words: series-connected lithium-ion batteries, dynamic time warping, multidimensional scaling, abnormal battery recognition

中图分类号:

TM911

钟国彬, 沈佳妮, 徐凯琪, 王乾坤, 贺益君, 苏伟, 马紫峰. 基于动态时间规整和多维标度策略的串联锂离子电池组异常电池可视化识别方法[J]. 储能科学与技术, 2019, 8(1): 180-190.

ZHONG Guobin, SHEN Jiani, XU Kaiqi, WANG Qiankun, HE Yijun, SU Wei, MA Zifeng. Dynamic time warping and multidimensional scaling approach based abnormal battery visual recognition for series-connected lithium-ion batteries pack[J]. Energy Storage Science and Technology, 2019, 8(1): 180-190.

参考文献

[1] 王莉, 谢乐琼, 张干, 等. 锂离子电池一致性筛选研究进展[J]. 储能科学与技术, 2018, 7(2):194-202. WANG L, XIE L Q, ZHANG G, et al. Research progress in the consistency screening of Li-ion batteries[J]. Energy Storage Science and Technology, 2018, 7(2):194-202.
[2] 王永琛, 倪江锋, 王海波, 等. 锂离子电池一致性分选方法[J]. 储能科学与技术, 2013, 2(5):522-527. WANG Y C, NI J F, WANG H B, et al. Sorting methods of lithium ion batteries consistency[J]. Energy Storage Science and Technology, 2013, 2(5):522-527.
[3] HE X M, ZHANG G, FENG X N, et al. A facile consistency screening approach to select cells with better performance consistency for commercial 18650 lithium ion cells[J]. International Journal of Electrochemistry Science, 2017, 12:1023-1025.
[4] SCHUSTER S F, BRAND M J, BERG P, et al. Lithium-ion cell-to-cell variation during battery electric vehicle operation[J]. Journal of Power Sources, 2015, 297:242-251.
[5] 王其钰, 王朔, 张杰男, 等. 锂离子电池失效分析概述[J]. 储能科学与技术, 2017, 6(5):1008-1025. WANG Q Y, WANG S, ZHANG J N, et al. Overview of the failure analysis of lithium ion batteries[J]. Energy Storage Science and Technology, 2017, 6(5):1008-1025.
[6] CASSANI P A, WILLIAMSON S S. Design, testing, and validation of a simplified control scheme for a novel plug-in hybrid electric vehicle battery cell equalizer[J]. IEEE Transactions on Industrial Electronics, 2010, 57(12):3956-3962.
[7] GALLARDO-LOZANO J, ROMERO-CADAVAL E, MILANES-MONTERO M I, et al. Battery equalization active methods[J]. Journal of Power Sources, 2014, 246:934-949.
[8] ZHENG Y J, OUYANG M G, LU L G, et al. On-line equalization for lithium-ion battery packs based on charging cell voltages (I):Equalization based on remaining charging capacity estimation[J]. Journal of Power Sources, 2014, 247:676-686.
[9] ZHENG Y J, OUYANG M G, LU L G, et al. On-line equalization for lithium-ion battery packs based on charging cell voltages (Ⅱ):Fuzzy logic equalization[J]. Journal of Power Sources, 2014, 247:460-466.
[10] PIAO C H, WANG Z G, CAO J, et al. Lithium-ion battery cell-balancing algorithm for battery management system based on real-time outlier detection[J]. Mathematical Problems in Engineering, 2015(2):1-12.
[11] 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.
[12] PIAO C H, HUANG Z, SU L, et al. Research on outlier detection algorithm for evaluation of battery system safety[J]. Advances in Mechanical Engineering, 2014, 2014(1):830402.
[13] WANG Z P, HONG J C, ZHANG L, et al. Voltage fault detection and precaution of batteries based on entropy and standard deviation for electric vehicles[J]. Energy Procedia, 2017, 105:2163-2168.
[14] LIU P, SUN Z Y, WANG Z P, et al. Entropy-based voltage fault diagnosis of battery systems for electric vehicles[J]. Energies, 2018, 11(1):136.
[15] QUINTIÁN H, CASTELEIRO-ROCA J L, PEREZ-CASTELO F J, et al. Hybrid intelligent model for fault detection of a lithium ion phosphate power cell used in electric vehicles[C]//International Conference on Hybrid Artificial Intelligent Systems, Springer International Publishing, 2016:141-147.
[16] KAYPMAZ T C, TUNCAY R N. An advanced cell model for diagnosing faults in operation of Li-ion polymer batteries[C]//Vehicle Power and Propulsion Conference. IEEE, 2011:1-5.
[17] KIANI M M. Online detection of faulty battery cells in energy storage systems via impulse response method[C]//Vehicle Power and Propulsion Conference. IEEE, 2011:1-6.
[18] DUMPALA S H, ALLURI K R, GANGASHETTY S V, et al. Analysis of constraints on segmental DTW for the task of query-by-example spoken term detection[C]//India Conference. IEEE, 2016:1-6.
[19] ANDRYSIAK T, ŁUKASZ S. Dynamic time warping analysis for security purposes in wireless sensor networks[C]//International Conference on Dependability and Complex Systems, Springer, Cham, 2018:1-12.
[20] HAN Z Y, ZHAO J, LEUNG H, et al. Construction of prediction intervals for gas flow systems in steel industry based on granular computing[J]. Control Engineering Practice, 2018, 78:79-88.
[21] BORG I. Multidimensional scaling[J]. Theory & Applications, 1997, 45(2):875-878.
[22] LÄGE D, RIEDEL M, MÖLLER H J. Exploring the structure of psychopathological symptoms:A re-analysis of AMDP data by robust nonmetric multidimensional scaling[J]. European Archives of Psychiatry & Clinical Neuroscience, 2012, 262(3):227-238.
[23] PAPILO P, MARIMIN, HAMBALI E, et al. Sustainability index assessment of palm oil-based bioenergy in Indonesia[J]. Journal of Cleaner Production, 2018, 196(20):808-820.
[24] JÄCKLE D, FISCHER F, SCHRECK T, et al. Temporal MDS plots for analysis of multivariate data[J]. IEEE Transactions on Visualization & Computer Graphics, 2016, 22(1):141-150.
[25] SHEN J N, HE Y J, MA Z F. Simultaneous model selection and parameter estimation for lithium-ion batteries:A sequential MINLP solution approach[J]. AIChE Journal, 2016, 62:78-89.
[26] SHEN J N, HE Y J, MA Z F, et al. Online state of charge estimation of lithium-ion batteries:A moving horizon estimation approach[J]. Chemical Engineering Science, 2016, 154:42-53.

基于动态时间规整和多维标度策略的串联锂离子电池组异常电池可视化识别方法

Dynamic time warping and multidimensional scaling approach based abnormal battery visual recognition for series-connected lithium-ion batteries pack

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