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 and Yide Wang
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Diju Gao: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China
Yong Zhou: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China
Tianzhen Wang: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China
Yide Wang: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China

Energies, 2020, vol. 13, issue 16, 1-13

Abstract: 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; particle filter (PF); remaining useful life (RUL); NARX neural 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: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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