A novel hybrid model based on ensemble strategy for lithium-ion battery residual life prediction

被引:0
作者
Li, Zheng [1 ,2 ]
Fang, Huajing [1 ,2 ]
Xiao, Zhouxiao [1 ,2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[2] Natl Key Lab Sci & Technol Multispectral Informat, Wuhan 430074, Hubei, Peoples R China
来源
2018 CHINESE AUTOMATION CONGRESS (CAC) | 2018年
基金
中国国家自然科学基金;
关键词
remaining useful life; hybrid model; ensemble strategy; error correction; REMAINING USEFUL LIFE; UNSCENTED KALMAN FILTER; STATE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As a power source widely used in electronic systems, the stability and safety of lithium-ion battery operation is of paramount importance. Generally, the decline in lithium-ion battery capacity can be used as a health indicator to track its degradation. This paper establishes a hybrid model to describe the capacity degradation of lithium-ion batteries, and proposes a novel ensemble strategy to improve the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries simultaneously. An empirical exponential model is selected to track the degradation trend of the battery, and the model parameters are updated by the unscented Kalman filter (UKF) algorithm. A homogeneous ensemble model based on the Bagging algorithm is generated to predict the residual evolution. The exponential model modified by residuals is introduced to predict the RUL of lithium-ion batteries. Simulation experiments on two kinds of batteries indicate that the proposed ensemble method can obtain accurate and stable predictions of RUL of lithium-ion batteries with strong robustness and less generalization errors.
引用
收藏
页码:2084 / 2089
页数:6
相关论文
共 17 条
[11]   Sparse Bayesian learning and the relevance vector machine [J].
Tipping, ME .
JOURNAL OF MACHINE LEARNING RESEARCH, 2001, 1 (03) :211-244
[12]   Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application [J].
Waag, Wladislaw ;
Kaebitz, Stefan ;
Sauer, Dirk Uwe .
APPLIED ENERGY, 2013, 102 :885-897
[13]  
Wan EA, 2000, ADV NEUR IN, V12, P666
[14]   An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks [J].
Wu, Ji ;
Zhang, Chenbin ;
Chen, Zonghai .
APPLIED ENERGY, 2016, 173 :134-140
[15]   An ensemble model for predicting the remaining useful performance of lithium-ion batteries [J].
Xing, Yinjiao ;
Ma, Eden W. M. ;
Tsui, Kwok-Leung ;
Pecht, Michael .
MICROELECTRONICS RELIABILITY, 2013, 53 (06) :811-820
[16]   A review on prognostics and health monitoring of Li-ion battery [J].
Zhang, Jingliang ;
Lee, Jay .
JOURNAL OF POWER SOURCES, 2011, 196 (15) :6007-6014
[17]   An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction [J].
Zheng, Xiujuan ;
Fang, Huajing .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2015, 144 :74-82