Using degradation-with-jump measures to estimate life characteristics of lithium-ion battery

Yin Shu, Qianmei Feng, Hao Liu

Research output: Contribution to journalArticle

Abstract

Degradation-with-jump measures are time series data sets containing the information of both continuous and randomly jumping degradation evolution of a system. Traditional maximum likelihood estimation and Bayesian estimation are not convenient for such general jump processes without closed-form distributions. Based on general degradation models derived using Lévy driven non-Gaussian Ornstein-Uhlenbeck (OU) processes, we propose a systematic statistical method using linear programing estimators and empirical characteristic functions. The point estimates of reliability function and lifetime moments are obtained by deriving their explicit expressions. We also construct bootstrap procedures for the confidence intervals. Simulation studies for a stable process and a stable driven OU process are performed. In the case study, we use a general Lévy process to fit the Li-ion battery life data, and then estimate the reliability and lifetime moments of the battery. By integrally analyzing degradation data series embedded with jump measures, our work provides the efficient and precise estimation for life characteristics.

Original languageEnglish (US)
Article number106515
JournalReliability Engineering and System Safety
Volume191
DOIs
StatePublished - Nov 1 2019
Externally publishedYes

Fingerprint

Degradation
Maximum likelihood estimation
Time series
Statistical methods
Lithium-ion batteries

Keywords

  • Autoregression (AR) models
  • Bootstrap
  • Empirical characteristic functions
  • Jump measures
  • Li-ion battery
  • Linear programing estimators
  • Non-Gaussian Ornstein-Uhlenbeck

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

Cite this

Using degradation-with-jump measures to estimate life characteristics of lithium-ion battery. / Shu, Yin; Feng, Qianmei; Liu, Hao.

In: Reliability Engineering and System Safety, Vol. 191, 106515, 01.11.2019.

Research output: Contribution to journalArticle

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