A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries

Tianfei Sun, Bizhong Xia, Yifan Liu, Yongzhi Lai, Weiwei Zheng, Huawen Wang, Wei Wang and Mingwang Wang
Additional contact information
Tianfei Sun: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Bizhong Xia: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Yifan Liu: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Yongzhi Lai: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Weiwei Zheng: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Huawen Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Wei Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Mingwang Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China

Energies, 2019, vol. 12, issue 19, 1-22

Abstract: The prognosis of lithium-ion batteries for their remaining useful life is an essential technology in prognostics and health management (PHM). In this paper, we propose a novel hybrid prediction method based on particle filter (PF) and extreme learning machine ( ELM ). First, we use ELM to simulate the battery capacity degradation trend. Second, PF is applied to update the random parameters of the ELM in real-time. An extreme learning machine prognosis model, based on particle filter (PFELM), is established. In order to verify the validity of this method, our proposed approach is compared with the standard ELM , the multi-layer perceptron prediction model, based on PF (PFMLP), as well as the neural network prediction model, based on bat-particle filter (BATPFNN), using the batteries testing datasets of the National Aeronautics and Space Administration (NASA) Ames Research Center. The results show that our proposed approach has better ability to simulate battery capacity degradation trends, better robustness, and higher Remaining Useful Life (RUL) prognosis accuracy than the standard ELM , the PFMLP, and the BATPFNN under the same conditions.

Keywords: lithium-ion batteries; remaining useful life (RUL); extreme learning machine ( ELM ); particle filter (PF) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/19/3678/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/19/3678/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:19:p:3678-:d:270913

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().