Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

Chunling Wu, Juncheng Fu (), Xinrong Huang, Xianfeng Xu and Jinhao Meng ()
Additional contact information
Chunling Wu: School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China
Juncheng Fu: School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China
Xinrong Huang: School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China
Xianfeng Xu: School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China
Jinhao Meng: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2023, vol. 16, issue 10, 1-16

Abstract: Accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a crucial reference for energy management of battery packs for electric vehicles. It is of great significance in ensuring safe and reliable battery operation while reducing maintenance costs of the battery system. To eliminate the nonlinear effects caused by factors such as capacity regeneration on the SOH sequence of batteries and improve the prediction accuracy and stability of lithium-ion battery SOH, a prediction model based on Variational Modal Decomposition (VMD) and Dung Beetle Optimization -Support Vector Regression (DBO-SVR) is proposed. Firstly, the VMD algorithm is used to decompose the SOH sequence of lithium-ion batteries into a series of stationary mode components. Then, each mode component is treated as a separate subsequence and modeled and predicted directly using SVR. To address the problem of difficult parameter selection for SVR, the DBO algorithm is used to optimize the parameters of the SVR model before training. Finally, the predicted values of each subsequence are added and reconstructed to obtain the final SOH prediction. In order to verify the effectiveness of the proposed method, the VMD-DBO-SVR model was compared with SVR, Empirical Mode Decomposition-Support Vector Regression (EMD-SVR), and VMD-SVR methods for SOH prediction of batteries based on the NASA dataset. Experimental results show that the proposed model has higher prediction accuracy and fitting degree, with prediction errors all within 1% and better robustness.

Keywords: lithium-ion battery; state of health; variational mode decomposition; dung beetle optimization algorithm; support vector regression (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/10/3993/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/10/3993/ (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:16:y:2023:i:10:p:3993-:d:1142857

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 ().