A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient

Diju Gao (1) , Yong Zhou (1) , Tianzhen Wang (1) , Yide Wang (2)

1 Shanghai Maritime University
2 IETR - Institut d'Électronique et des Technologies du numéRique

With the wide application of lithium batteries, battery fault prediction and healthmanagement have become more and more important. This article proposes a method for predictingthe remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems causedby continuing to use the battery after reaching its service life threshold. Since the battery capacityis not easy to obtain online, we propose that some measurable parameters should be used in thebattery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace themeasured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) topredict the RUL of the lithium batteries. Simulation results show that the proposed method has highprediction accuracy, stability, and practical value.

Keywords: lithium-ion battery; particle filter (PF); remaining useful life (RUL); NARX neural network

Domains: Electronics; Other

Diju Gao, Yong Zhou, Tianzhen Wang, Yide Wang. A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient. Energies, 2020, 13 (16), pp.4183. ⟨10.3390/en13164183⟩. ⟨hal-02919954⟩