Work

Fractional Poisson Process for Estimation of Capacity Degradation in Li-Ion Batteries by Walk Sequences

Year: 2025

Type: article

Abstract: Each charging/discharging cycle leads to a gradual decrease of the battery’s capacity. Degradation of capacity in lithium-ion batteries represents a non-monotonous process with random jumps. Earlier studies claimed that the instantaneous degradation value of lithium-ion battery is influenced by the historical data, demonstrating long-range dependence. The existing methods ignore large jumps and long-range dependence in degradation processes. In order to capture long-range dependent property and random jumps, we deal with the fractional Poisson process. We also outline the relationship between the long-range correlation and the Hurst index. The connection between random jumps in capacitance and long-range dependence of the fractional Poisson process is proven. In order to construct the fractional Poisson predictive model, we included the fractional Brownian motion as the diffusion term and the fractional Poisson process as the jump term. The efficiency of our approach for calculation of the random walk was tested on the NASA’s dataset of Li-ion battery dataset. We claim that the predictive model based on the fractional Poisson process has a number of advantages over the fractional Brownian motion, fractional Levy stable motion, the Wiener model and the Long-Short Term Memory model.

Source: Fractal and Fractional

DOI: 10.3390/fractalfract9090558

Authors: Jing Shi, Feng Liu, Aleksey Kudreyko, Zhengyang Wu, Wanqing Song

Institutions: Minnan University of Science and Technology, Anyang University, Ufa University of Science and Technology

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: Fujian Provincial Department of Science and Technology