Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm

被引：92

作者：

Wang, Haiyang ^{[1
]}

Song, Wanqing ^{[1
]}

Zio, Enrico ^{[2
]}

Kudreyko, Aleksey ^{[3
]}

Zhang, Yujin ^{[1
]}

机构：

[1] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai, Peoples R China

[2] Politecn Milan, Energy Dept, Campus Bovisa,Via La Masa 34-3, I-20156 Milan, MI, Italy

[3] Bashkir State Med Univ, Dept Med Phys & Informat, Lenina St 3, Ufa 450008, Russia

来源：

MEASUREMENT | 2020年 / 161卷

基金：

上海市自然科学基金;

关键词：

Remaining useful life; Long-range dependence; Fractional Brownian motion; Hurst exponent; Maximum likelihood estimation; Fruit-fly Optimization Algorithm; MODEL;

D O I：

10.1016/j.measurement.2020.107904

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, a novel method base on non-Markovian Fractional Brownian Motion (FBM) is proposed for Lithium-ion batteries remaining useful life (RUL) prediction. Firstly, the FBM degradation model is introduced and the Hurst exponent (H) is calculated. Secondly, the parameters of the FBM model are estimated by maximum likelihood estimation (MLE). The Fruit-fly Optimization Algorithm (FOA) is proposed to optimize the H. Then the procedure for RUL prediction is provided. Capacity degradation data of Lithium-ion batteries is selected as prediction case, and the RUL prediction results are given by two real cases of RUL prediction for lithium-ion batteries. The validity of the proposed method is verified by several evaluation criteria. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页数：9

共 32 条

[1] Remaining useful life estimation: review [J].

Ahmadzadeh F. ;

Lundberg J. .

International Journal of System Assurance Engineering and Management, 2014, 5 (04) :461-474

[2]

Brian B., 2014, P ANN C PROGN HLTH

[3]

Dai W., 1996, J. Appl. Math. Stochastic Anal, V9, P439, DOI DOI 10.1155/S104895339600038X

[4] Remaining useful life prediction of lithium-ion battery based on extended Kalman particle filter [J].

Duan, Bin ;

Zhang, Qi ;

Geng, Fei ;

Zhang, Chenghui .

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2020, 44 (03) :1724-1734

[5] Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis [J].

Gao, Yangde ;

Villecco, Francesco ;

Li, Ming ;

Song, Wanqing .

ENTROPY, 2017, 19 (04)

[6] A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm [J].

Khumprom, Phattara ;

Yodo, Nita .

ENERGIES, 2019, 12 (04)

[7] A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter [J].

Li, Fan ;

Xu, Jiuping .

MICROELECTRONICS RELIABILITY, 2015, 55 (07) :1035-1045

[8] Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks [J].

Li, Xiaoyu ;

Zhang, Lei ;

Wang, Zhenpo ;

Dong, Peng .

JOURNAL OF ENERGY STORAGE, 2019, 21 :510-518

[9] A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics [J].

Liu, Datong ;

Zhou, Jianbao ;

Liao, Haitao ;

Peng, Yu ;

Peng, Xiyuan .

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2015, 45 (06) :915-928

[10] An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation [J].

Liu, Datong ;

Xie, Wei ;

Liao, Haitao ;

Peng, Yu .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2015, 64 (03) :660-670

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