A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD

Ling Mao, Jie Xu, Jiajun Chen, Jinbin Zhao, Yuebao Wu and Fengjun Yao
Additional contact information
Ling Mao: School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China
Jie Xu: School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China
Jiajun Chen: Pegasus Power Energy Co., Ltd., Hangzhou 310019, China
Jinbin Zhao: School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China
Yuebao Wu: School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China
Fengjun Yao: School of Automation, Shanghai University of Electric Power, No.2588, Changyang Road, Yangpu District, Shanghai 200090, China

Energies, 2020, vol. 13, issue 9, 1-13

Abstract: To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

Keywords: LSTM-STW; GS-LM; lithium-ion battery; RUL prediction; EEMD; higher accuracy; capacity sudden increase; prediction starting point (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/9/2380/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/9/2380/ (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:13:y:2020:i:9:p:2380-:d:356008

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 ().