Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Abstract

In this paper, an adaptive remaining useful life prediction model is proposed for electric vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit variability is considered a random variable. To facilitate the application, the convergence of the RUL prediction model is proved. Replacement of the lithium battery in the electric car is recommended according to the remaining useful life prediction results. The effectiveness of the proposed model is shown in the case study.

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

“…There is also a limitation to the proposed model, which is the statistical criterion for the adaptive fGPm. In the future, we will extend the model to the RUL prediction of lithium batteries in energy storage facilities, where the non-Gaussian assumption and long memory effects are common [41]. Compared with these existing methods, the proposed RUL prediction model possesses more interpretability.…”

“…Fractal Fract. 2024, 8, 14 14 of 16 will extend the model to the RUL prediction of lithium batteries in energy storage facilities, where the non-Gaussian assumption and long memory effects are common [41].…”