Remaining Useful Life Prediction of Lithium-Ion Battery via a Sequence Decomposition and Deep Learning Integrated Approach

被引:53
作者
Chen, Zhang [1 ]
Chen, Liqun [2 ]
Shen, Wenjing [1 ]
Xu, Kangkang [3 ]
机构
[1] Shenzhen Technol Univ, Sino German Coll Intelligent Mfg, Shenzhen 518118, Peoples R China
[2] Shenzhen Technol Univ, Coll Urban Transportat & Logist, Shenzhen 518118, Peoples R China
[3] Guangdong Univ Technol, Sch Electromech Engn, Guangzhou 510006, Peoples R China
基金
中国国家自然科学基金;
关键词
Predictive models; Degradation; Adaptation models; Computational modeling; Data models; Mathematical models; Deep learning; Lithium-ion battery; remaining useful life prediction; sequence decomposition; deep learning; capacity degradation; STATE;
D O I
10.1109/TVT.2021.3134312
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
The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is of great importance to the health management of electric vehicles and hybrid electric vehicles. However, fluctuation and nonlinearity occur during battery degradation, resulting in difficulties in both model adaptability and RUL prediction accuracy. To face the challenge, we propose a sequence decomposition and deep learning integrated prognostic approach for the RUL prediction of LIBs. Complementary ensemble empirical mode decomposition and principal component analysis are applied to separate the local fluctuations and the global degradation trend from the battery aging data. The long short-term memory neural network combined with fully connected layers is designed as a transfer learning model. The hyperparameter optimization and finetuning strategy of the model is developed based on offline training data. In addition, to further realize the reasonable and effective LIB second-life applications, the RUL corresponding to different failure thresholds is predicted. The performance of the proposed integrated approach in degradation modeling and RUL prediction is evaluated on three publicly available LIB datasets with different degradation characteristics, as well as compared with other prediction algorithms under the same conditions. The illustrative results demonstrate that the proposed approach can achieve accurate, adaptive, and robust prediction for both capacity trajectory and RUL.
引用
收藏
页码:1466 / 1479
页数:14
相关论文
共 43 条
[1]   Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery [J].
Bartlett, Alexander ;
Marcicki, James ;
Onori, Simona ;
Rizzoni, Giorgio ;
Yang, Xiao Guang ;
Miller, Ted .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2016, 24 (02) :384-399
[2]   A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery [J].
Chang, Yang ;
Fang, Huajing ;
Zhang, Yong .
APPLIED ENERGY, 2017, 206 :1564-1578
[3]   Recycling End-of-Life Electric Vehicle Lithium-Ion Batteries [J].
Chen, Mengyuan ;
Ma, Xiaotu ;
Chen, Bin ;
Arsenault, Renata ;
Karlson, Peter ;
Simon, Nakia ;
Wang, Yan .
JOULE, 2019, 3 (11) :2622-2646
[4]  
Chen Z., 2021, ENERGY, V234, P1
[5]   Active Adaptive Battery Aging Management for Electric Vehicles [J].
Corno, Matteo ;
Pozzato, Gabriele .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (01) :258-269
[6]   A rest-time-based prognostic model for remaining useful life prediction of lithium-ion battery [J].
Deng, Liming ;
Shen, Wenjing ;
Wang, Hongfei ;
Wang, Shuqiang .
NEURAL COMPUTING & APPLICATIONS, 2021, 33 (06) :2035-2046
[7]  
Ding P, RENEWABLE SUSTAIN EN, V148, P1
[8]   Data-Driven Battery Health Prognosis Using Adaptive Brownian Motion Model [J].
Dong, Guangzhong ;
Yang, Fangfang ;
Wei, Zhongbao ;
Wei, Jingwen ;
Tsui, Kwok-Leung .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (07) :4736-4746
[9]   State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method [J].
Gou, Bin ;
Xu, Yan ;
Feng, Xue .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (10) :10854-10867
[10]   Online Estimation of the Electrochemical Impedance Spectrum and Remaining Useful Life of Lithium-Ion Batteries [J].
Guha, Arijit ;
Patra, Amit .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2018, 67 (08) :1836-1849