Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

被引:393
作者
Liu, Datong [1 ]
Pang, Jingyue [1 ]
Zhou, Jianbao [1 ]
Peng, Yu [1 ]
Pecht, Michael [2 ]
机构
[1] Harbin Inst Technol, Dept Automat Test & Control, Harbin 150080, Peoples R China
[2] Univ Maryland, CALCE, College Pk, MD 20742 USA
基金
高等学校博士学科点专项科研基金; 美国国家科学基金会;
关键词
FRAMEWORK;
D O I
10.1016/j.microrel.2013.03.010
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. At present, many, algorithms have been applied to perform prognostics for SOH estimation, especially data-driven prognostics algorithms supporting uncertainty representation and management. To describe the uncertainty in evaluation and prediction, we used the Gaussian Process Regression (GPR), a data-driven approach, to perform SOH prediction with mean and variance values as the uncertainty representation of SOH. Then, in order to realize multiple-step-ahead prognostics, we utilized an improved GPR method-combination Gaussian Process Functional Regression (GPFR)-to capture the actual trend of SOH, including global capacity degradation and local regeneration. Experimental results confirm that the proposed method can be effectively applied to lithium-ion battery monitoring and prognostics by quantitative comparison with the other GPR and GPFR models. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:832 / 839
页数:8
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