基于几何特征与流形距离的锂电池健康评估

doi:10.6040/j.issn.1672-3961.0.2017.268

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 157-165.doi: 10.6040/j.issn.1672-3961.0.2017.268

基于几何特征与流形距离的锂电池健康评估

包塔拉^1,2,马剑^1,2*,甘祖旺^1,2

1. 北京航空航天大学可靠性与系统工程学院, 北京 100191; 2.北京航空航天大学可靠性与环境工程技术重点实验室, 北京 100191

收稿日期:2017-02-10 出版日期:2017-10-20 发布日期:2017-02-10
通讯作者: 马剑(1986— ),男,甘肃兰州人,讲师,博士,主要研究方向为故障诊断,故障预测与系统健康管理等.E-mail:majian3128@126.com E-mail:btala@126.com
作者简介:包塔拉(1991— ),男,内蒙古通辽人,硕士研究生,主要研究方向为故障诊断,故障预测与系统健康管理等. E-mail:btala@126.com
基金资助:
国家自然科学基金资助项目(51575021,51105019,51605014);国防技术基础基金资助项目(Z1320113B002)

Performance assessment of lithium-ion battery based on geometric features and manifold distance

BAO Tala^1,2, MA Jian^1,2*, GAN Zuwang^1,2

1. School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China;
2. Science &
Technology Laboratory on Reliability and Environmental Engineering, Beihang University, Beijing 100191, China

Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

摘要/Abstract

摘要： 作为实现电池健康管理的有效途径,精准的荷电状态估计和健康衰退状况评估能够很好的解决锂离子电池在实际使用过程中面临的可靠使用和安全管理问题。考虑到现有电池健康衰退状况评估方法将监测数据放在欧氏空间进行分析,因而扭曲了数据的本质结构导致工况适用性差,利用流形学习挖掘隐藏在电池监测数据中的健康信息,并在流形空间中对锂离子电池健康状态进行度量。进行实例分析并对该方法的有效性进行了验证。

关键词: 锂电池, 流形学习, 健康状态, 几何特征

Abstract: The estimations of state of charge and state of health evolved in li-ion battery health management systems can help managing the reliability and safety of the fielded battery. Considering that many data-driven state of health estimations habituated to model the battery monitoring information in Euclid space with the purpose of assessing battery health status, which often brings about a poor adaptability to operation conditions, manifold learning was used to mine the health information hidden in the battery monitoring data and manifold distance was utilized to measure the battery health condition. At last, a case analysis was conducted to validate the proposed state of health estimation method for the li-ion battery.

Key words: lithium-ion battery, health condition, manifold learning, geometric features

中图分类号:

TP277

包塔拉,马剑,甘祖旺. 基于几何特征与流形距离的锂电池健康评估[J]. 山东大学学报(工学版), 2017, 47(5): 157-165.

BAO Tala, MA Jian, GAN Zuwang. Performance assessment of lithium-ion battery based on geometric features and manifold distance[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 157-165.

参考文献

[1] SHIM J, KOSTECKI R, RICHARDSON T. Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature[J]. Journal of Power Sources, 2002, 112(1):222-230.
[2] ZHANG X, ROSSP, KOSTECKI R. Diagnostic characterization of high power lithium-ion batteries for use in hybride lectric vehicles[J].Journal of the Electrochemical Society, 2001, 148(5):A463-A470.
[3] LI Y, OMAR N, NANINI-MAURY E, et al. Performance and reliability assessment of NMC lithium ion batteries for stationary application[C] //Processdings of IEEE on Vehicle Power and Propulsion 2016. Hangzhou, China:VPPC, 2016:137.
[4] ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
[5] AN D, CHOI J H, KIM N H. Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab[J]. Reliability Engineering & System Safety, 2013, 115: 161-169.
[6] 王靖. 流形学习的理论与方法研究[D]. 杭州: 浙江大学, 2006. WANG Jing. Research on manifold learning:theories and approaches[D]. Hangzhou: Zhejiang University, 2006.
[7] 曹丽. 基于流形的特征抽取及人脸识别研究[D].扬州:扬州大学, 2009. CAO Li. Manifold-based feature extraction and face recognition analysis[D].Yangzhou:Yangzhou University, 2009.
[8] TENENBAUM J B, DE SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.
[9] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.
[10] BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural computation, 2003, 15(6): 1373-1396.
[11] 李小丽, 薛清福. 几种流形学习算法的比较研究[J]. 电脑与信息技术, 2009,17(3): 14-18. LI Xiaoli, XUE Qingfu. A comparative study of some manifold learning algorithms[J]. Computer and Information Technology, 2009, 17(3):14-18.
[12] HE X, NIYOGI P. Locality preserving projections[C] //Proceedings of Neural Information Processing Systems on Computer Science. Carson City, USA: NIPS, 2003:153.
[13] GONG M, BO L, WANG L, et al. Image texture classification using a manifold-distance-based evolutionary clustering method[J]. Optical Engineering, 2008, 47(7): 77201-77210.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于几何特征与流形距离的锂电池健康评估

Performance assessment of lithium-ion battery based on geometric features and manifold distance

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

多维度评价

本文评价

推荐阅读 0

[1]	董满,刘淑琴. 基于UKF和AH法的磁悬浮人工心脏泵用锂电池SOC估计复合算法[J]. 山东大学学报(工学版), 2018, 48(2): 121-127.
[2]	张振月,李斐,江铭炎. 基于低秩表示投影的无监督人脸特征提取[J]. 山东大学学报(工学版), 2018, 48(1): 15-20.
[3]	王洪元,封磊,冯燕,程起才. 流形学习算法在中文文本分类中的应用[J]. 山东大学学报(工学版), 2012, 42(4): 8-12.
[4]	贺广南，杨育彬*. 基于流形学习的图像检索算法研究[J]. 山东大学学报(工学版), 2010, 40(5): 129-136.
[5]	谭台哲,梁应毅,刘富春. 一种ReliefF特征估计方法在无监督流形学习中的应用[J]. 山东大学学报(工学版), 2010, 40(5): 66-71.