Literature DB >> 34305335

Prognostics for lithium-ion batteries using a two-phase gamma degradation process model.

Chun Pang Lin1,2, Man Ho Ling3, Javier Cabrera4, Fangfang Yang5, Denis Yau Wai Yu6, Kwok Leung Tsui1,7.   

Abstract

To address the degradation of rechargeable batteries, this paper presents a two-phase gamma process model with a fixed change-point for modeling the voltage-discharge curves of battery cycle aging under a constant current. The model can be applied to estimate the state of charge (SOC) and the remaining useful discharge time (RUT) in a cycle with consideration of the effect of cycle aging, and can also be applied to estimate the state of life (SOL) and the remaining useful life (RUL) across cycles. The applications of the proposed model are demonstrated using the experimental cycle aging data of a lithium iron phosphate battery. A comparison shows that the proposed model generates a more accurate prediction than the conventional two-term exponential model with capacity data under a particle filter framework, and this reveals the superiority of modeling with voltage over modeling with capacity. The analytical expression of mean useful discharge time in a cycle (or mean time to failure) is developed with approximation by a Taylor expansion and the Birnbaum-Saunders distribution, and the result is shown to be in good agreement with the true mean of a gamma process.

Entities:  

Keywords:  battery cycle aging; degradation modeling; gamma process; remaining useful life prediction; state of charge; state of life

Year:  2021        PMID: 34305335      PMCID: PMC8294100          DOI: 10.1016/j.ress.2021.107797

Source DB:  PubMed          Journal:  Reliab Eng Syst Saf        ISSN: 0951-8320            Impact factor:   7.247


  2 in total

1.  Accelerated degradation models for failure based on geometric Brownian motion and gamma processes.

Authors:  Chanseok Park; W J Padgett
Journal:  Lifetime Data Anal       Date:  2005-12       Impact factor: 1.588

Review 2.  30 Years of Lithium-Ion Batteries.

Authors:  Matthew Li; Jun Lu; Zhongwei Chen; Khalil Amine
Journal:  Adv Mater       Date:  2018-06-14       Impact factor: 30.849

  2 in total

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