A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Xiaoqiong Pang, Rui Huang, Jie Wen, Yuanhao Shi, Jianfang Jia and Jianchao Zeng
Additional contact information
Xiaoqiong Pang: School of Data Science and Technology, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Rui Huang: School of Data Science and Technology, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Jie Wen: School of Electrical and Control Engineering, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Yuanhao Shi: School of Electrical and Control Engineering, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Jianfang Jia: School of Electrical and Control Engineering, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Jianchao Zeng: School of Data Science and Technology, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China

Energies, 2019, vol. 12, issue 12, 1-14

Abstract: Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

Keywords: lithium-ion battery; remaining useful life; regeneration phenomenon; wavelet decomposition; NAR neural network (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 (19)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/12/2247/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/12/2247/ (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:12:p:2247-:d:239228

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 ().