Remaining Useful Life Prediction of Lithium-Ion Battery Based on Adaptive Fractional Lévy Stable Motion with Capacity Regeneration and Random Fluctuation Phenomenon

The capacity regeneration phenomenon is often overlooked in terms of prediction of the remaining useful life (RUL) of LIBs for acceptable fitting between real and predicted results. In this study, we suggest a novel method for quantitative estimation of the associated uncertainty with the RUL, which is based on adaptive fractional Levy stable motion (AfLSM) and integrated with the Mellin-Stieltjes transform and Monte Carlo simulation. The proposed degradation model exhibits flexibility for capturing long-range dependence, has a non-Gaussian distribution, and accurately describes heavy-tailed properties. Additionally, the nonlinear drift coefficients of the model can be adaptively updated on the basis of the degradation trajectory. The performance of the proposed RUL prediction model was verified by using the University of Maryland CALEC dataset. Our forecasting results demonstrate the high accuracy of the method and its superiority over other state-of-the-art methods.

Automated summary

This study proposes a method based on adaptive fractional Levy stable motion (AfLSM), Mellin-Stieltjes transform, and Monte Carlo simulation for accurate estimation of the remaining useful life (RUL) of lithium-ion batteries. The proposed model exhibits flexibility in capturing long-range dependence, has a non-Gaussian distribution and heavy-tailed properties, and allows for adaptive updates of the drift coefficients based on the degradation trajectory.