%0 Journal Article
%T 基于几何特征与流形距离的锂电池健康评估<br>Performance assessment of lithium-ion battery based on geometric features and manifold distance
%A 包塔拉
%A 马剑
%A 甘祖旺&lt
%A br&gt
%A BAO Tala
%A MA Jian
%A GAN Zuwang
%J 山东大学学报（工学版）
%D 2017
%R 10.6040/j.issn.1672-3961.0.2017.268
%X 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。<br>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
%K 锂电池
%K 流形学习
%K 健康状态
%K 几何特征
%K &lt
%K br&gt
%K lithium-ion battery
%K health condition
%K manifold learning
%K geometric features
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.268