Online Estimation of Lithium-Ion Battery State of Health Using Grey Neural Network

被引：0
|
作者：
Wei H. [1 ]
Chen X. [1 ]
Lü Z. [1 ]
Wang Z. [1 ]
Pan H. [1 ]
Chen L. [1 ]
机构：
[1] School of Mechanical Engineering, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region
来源：
Chen, Lin (gxdxcl@163.com) | 2017年 / Power System Technology Press卷 / 41期
基金：
中国国家自然科学基金;
关键词：
Grey neural network; Health indicator; Lithium ion battery; SOH estimation;
D O I：
10.13335/j.1000-3673.pst.2017.0522
中图分类号：
学科分类号：
摘要：
Lithium-ion battery is a complex electrochemical dynamic system. It is difficult to achieve online estimation of state of health (SOH) by single monitoring of physical and chemical properties of the battery. In this paper, increases of internal resistance and polarization resistance and decrease of polarization capacitance are proposed as new health indicators (HIs) of the battery. Grey neural network is used to train the HIs as input for grey neural network model and battery capacity degradation as its output. Finally, battery SOH estimates are achieved through online construction of battery HIs. Experimental results show that the proposed HIs can effectively characterize battery health state. The grey neural network degradation model has higher online SOH estimation accuracy with estimation error less than 2%, lower than that obtained with BP neural network model. © 2017, Power System Technology Press. All right reserved.
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收藏
页码：4038 / 4044
页数：6
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