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

Wujin Deng, Yan Gao, Jianxue Chen, Aleksey Kudreyko, Carlo Cattani, Enrico Zio () and Wanqing Song
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Wujin Deng: Shanghai University of Engineering Science
Yan Gao: Shanghai University of Engineering Science
Jianxue Chen: Shanghai University of Engineering Science
Aleksey Kudreyko: BASHEDU - Bashkir State University
Enrico Zio: CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres, POLIMI - Politecnico di Milano [Milan]
Wanqing Song: Shanghai University of Engineering Science

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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.

Keywords: 1/f noise; age and state-dependent adaptive model; electric vehicle lithium battery; long-range dependence; multi-fractal Weibull motion; remaining useful life (search for similar items in EconPapers)
Date: 2023-04
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Published in Entropy, 2023, 25 (4), pp.646. ⟨10.3390/e25040646⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04103676

DOI: 10.3390/e25040646

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