预言
健康状况
估计
锂离子电池
计算机科学
集合(抽象数据类型)
人工智能
功率(物理)
数据挖掘
可靠性工程
工程类
电池(电)
物理
系统工程
量子力学
程序设计语言
作者
Yuanwang Deng,Hejie Ying,E Jiaqiang,Hao Zhu,Kexiang Wei,Jingwei Chen,Feng Zhang,Gaoliang Liao
出处
期刊:Energy
[Elsevier]
日期:2019-03-29
卷期号:176: 91-102
被引量:168
标识
DOI:10.1016/j.energy.2019.03.177
摘要
In order to provide an accurate State-Of-Health (SOH) estimation, a novel estimation method is proposed in this paper. In this work, some battery SOH relate features are selected theoretically, proved and then re-screened mathematically. These features can reflect the battery degeneration from different aspects. Also, a new training set design idea is proposed for Least Squares Support Vector Machine algorithm, thereby a model that is suitable for lithium-ion Battery SOH estimation under multi-working conditions can be built. Several lithium-ion battery degeneration testing datasets from National Aeronautics and Space Administration Ames Prognostics Center of Excellence are used to validate the proposed method. Results demonstrate both the superiority of the proposed method and its potential applicability as an effective SOH estimation method for embedded Battery Management System.
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