Early-Stage end-of-Life prediction of lithium-Ion battery using empirical mode decomposition and particle filter

The predictive maintenance is a major challenge for improving battery safety without compromising performance. Its main objective is to predict the end-of-life (EOL) and assess the associated prediction uncertainty. In this paper, we consider this issue and propose a hybrid method combining empirica...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part A, Journal of power and energy Journal of power and energy, 2023-08, Vol.237 (5), p.1090-1099
Main Authors: Meng, Jianwen, Azib, Toufik, Yue, Meiling
Format: Article
Language:English
Subjects:
Algorithms
End of life
Engineering Sciences
Life cycle engineering
Life prediction
Lithium-ion batteries
Predictive maintenance
Product safety
Rechargeable batteries
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The predictive maintenance is a major challenge for improving battery safety without compromising performance. Its main objective is to predict the end-of-life (EOL) and assess the associated prediction uncertainty. In this paper, we consider this issue and propose a hybrid method combining empirical mode decomposition (EMD) and particle filter (PF) to perform the battery early EOL prediction and its uncertainty assessment. The proposed approach is tested on the two most widely accessible public lithium-ion battery degradation datasets from the Prognostics Center of Excellence at NASA Ames and the Center for Advanced Life Cycle Engineering at the University of Maryland. To avoid the overfitting, a major constraint in the prediction phase, only the residual sequence obtained after EMD decomposition of the raw data is used as the measurement input for PF. Besides, the sum of standard deviation of all intrinsic mode functions (IMFs) is used to determine the noise characteristic for the PF algorithm. Extensive experiments are conducted to show the importance of uncertainty assessment for the early EOL prediction. Although the distance between the true EOL and the mean predicted EOL has no obvious decrease when more operation data is available, the results show a clear decreasing trend of EOL prediction uncertainty when the prediction starts from later operation cycles. Particularly, a strong experimental support is provided for filtering-based prediction methods in the early EOL prediction.
ISSN:0957-6509
2041-2967
DOI:10.1177/09576509231153907