Indirect Prediction of Lithium-Ion Battery RUL Based on CEEMDAN and CNN-BiGRU

Kai Lv, Zhiqiang Ma (), Caijilahu Bao and Guangchen Liu
Additional contact information
Kai Lv: School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
Zhiqiang Ma: School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
Caijilahu Bao: School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
Guangchen Liu: Engineering Research Center of Large-Scale Energy Storage Technology, Ministry of Education, Hohhot 010080, China

Energies, 2024, vol. 17, issue 7, 1-19

Abstract: Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability.

Keywords: lithium-ion battery; capacity regeneration; remaining useful life; complete ensemble empirical mode decomposition with adaptive noise; bidirectional gate recurrent unit 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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/7/1704/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/7/1704/ (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:17:y:2024:i:7:p:1704-:d:1369160

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 ().