LAPSE:2023.25820
Published Article
LAPSE:2023.25820
A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient
Diju Gao, Yong Zhou, Tianzhen Wang, Yide Wang
March 29, 2023
With the wide application of lithium batteries, battery fault prediction and health management have become more and more important. This article proposes a method for predicting the remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems caused by continuing to use the battery after reaching its service life threshold. Since the battery capacity is not easy to obtain online, we propose that some measurable parameters should be used in the battery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace the measured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) to predict the RUL of the lithium batteries. Simulation results show that the proposed method has high prediction accuracy, stability, and practical value.
Keywords
lithium-ion battery, NARX neural network, particle filter (PF), remaining useful life (RUL)
Suggested Citation
Gao D, Zhou Y, Wang T, Wang Y. A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient. (2023). LAPSE:2023.25820
Author Affiliations
Gao D: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China
Zhou Y: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China
Wang T: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China [ORCID]
Wang Y: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China; Institut d’Électronique et des Technologies du numéRique, UMR CNRS 6164, Universite de Nantes, F-44000 Nantes, Franc [ORCID]
Journal Name
Energies
Volume
13
Issue
16
Article Number
E4183
Year
2020
Publication Date
2020-08-13
Published Version
ISSN
1996-1073
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PII: en13164183, Publication Type: Journal Article
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doi:10.3390/en13164183
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