Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries

被引：174

作者：

Deng, Yuanwang ^{[1
]}

Ying, Hejie ^{[1
]}

Jiaqiang, E. ^{[1
,3
]}

Zhu, Hao ^{[1
,3
]}

Wei, Kexiang ^{[2
]}

Chen, Jingwei ^{[1
,3
]}

Zhang, Feng ^{[1
,3
]}

Liao, Gaoliang ^{[1
,3
]}

机构：

[1] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China

[2] Hunan Inst Engn, Hunan Prov Key Lab Vehicle Power & Transmiss Syst, Xiangtan 411104, Peoples R China

[3] Hunan Univ, Inst New Energy & Energy Saving & Emiss Reduct Te, Changsha 410082, Hunan, Peoples R China

来源：

ENERGY | 2019年 / 176卷

关键词：

Lithium-ion battery; State-of-Health; Least squares Support Vector Machine; Feature selection; Multi-working conditions; GAUSSIAN PROCESS REGRESSION; SUPPORT VECTOR REGRESSION; DIESEL PARTICULATE FILTER; ENERGY-CONSUMPTION; CHARGE; COMBUSTION; PREDICTION; PERFORMANCE; PROGNOSTICS; EMISSIONS;

D O I：

10.1016/j.energy.2019.03.177

中图分类号：

O414.1 [热力学];

学科分类号：

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. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页码：91 / 102

页数：12

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