Lifetime prognosis of lithium-ion batteries through novel accelerated degradation measurements and a combined gamma process and Monte Carlo method

Yu Chang Lin, Kuan-Jung Chung

Research output: Contribution to journalArticle

Abstract

A compositional prognostic-based assessment using the gamma process and Monte Carlo simulation was implemented to monitor the likelihood values of test Lithium-ion batteries on the failure threshold associated with capacity loss. The evaluation of capacity loss for the test LiFePO4 batteries using a novel dual dynamic stress accelerated degradation test, called D 2 SADT, to simulate a situation when driving an electric vehicle in the city. The Norris and Landzberg reliability model was applied to estimate activation energy of the test batteries. The test results show that the battery capacity always decreased at each measurement time-step during D2SADT to enable the novel test method. The variation of the activation energies for the test batteries indicate that the capacity loss of the test battery operated under certain power and temperature cycling conditions, which can be accelerated when the charge-discharge cycles increase. Moreover, the modeling results show that the gamma process combined with Monte Carlo simulations provides superior accuracy for predicting the lifetimes of the test batteries compared with the baseline lifetime data (i.e., real degradation route and lifetimes). The results presented high prediction quality for the proposed model as the error rates were within 5% and were obtained for all test batteries after a certain quantity of capacity loss, and remained so for at least three predictions.

Original languageEnglish
Article number559
JournalApplied Sciences (Switzerland)
Volume9
Issue number3
DOIs
Publication statusPublished - 2019 Feb 8

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prognosis
Monte Carlo method
electric batteries
Monte Carlo methods
lithium
degradation
Degradation
life (durability)
ions
Activation energy
Electric vehicles
Time measurement
Lithium-ion batteries
activation energy
cycles
predictions
vehicles
Temperature
simulation
routes

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cite this

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abstract = "A compositional prognostic-based assessment using the gamma process and Monte Carlo simulation was implemented to monitor the likelihood values of test Lithium-ion batteries on the failure threshold associated with capacity loss. The evaluation of capacity loss for the test LiFePO4 batteries using a novel dual dynamic stress accelerated degradation test, called D 2 SADT, to simulate a situation when driving an electric vehicle in the city. The Norris and Landzberg reliability model was applied to estimate activation energy of the test batteries. The test results show that the battery capacity always decreased at each measurement time-step during D2SADT to enable the novel test method. The variation of the activation energies for the test batteries indicate that the capacity loss of the test battery operated under certain power and temperature cycling conditions, which can be accelerated when the charge-discharge cycles increase. Moreover, the modeling results show that the gamma process combined with Monte Carlo simulations provides superior accuracy for predicting the lifetimes of the test batteries compared with the baseline lifetime data (i.e., real degradation route and lifetimes). The results presented high prediction quality for the proposed model as the error rates were within 5{\%} and were obtained for all test batteries after a certain quantity of capacity loss, and remained so for at least three predictions.",
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