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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

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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. 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 Tala1,2, MA Jian1,2*, GAN Zuwang1,2   

  1. 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: 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
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