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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.
Highlights
Lithium-ion batteries have the characteristics of low self-discharge rate, good safety performance, fast charging and discharging capabilities, and high output power
Literature in [5] improved the prediction performance of remaining useful life (RUL) by introducing the unscented particle filter (UPF). This PF-based prediction method usually needs to track the system state based on known capacity data
Because the standard PF algorithm has problems of particle degradation and insufficient samples, which may cause deviations in the obtained results, we introduce the PF based on the Kendall rank correlation coefficient (KCCPF) into the battery RUL prediction
Summary
Lithium-ion batteries have the characteristics of low self-discharge rate, good safety performance, fast charging and discharging capabilities, and high output power. They have been widely used in almost all industrial energy supply fields [1,2,3]. Literature in [5] improved the prediction performance of RUL by introducing the unscented particle filter (UPF). This PF-based prediction method usually needs to track the system state based on known capacity data. The capacity data are often difficult to obtain accurately during use of the battery, and the PF algorithm itself has problems of particle degradation and sample shortage, which limit its applications.
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