Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method

Xiaopeng TANG, Ke YAO, Changfu ZOU, Boyang LIU, Furong GAO*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)


Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.

Original languageEnglish
Pages (from-to)2456-2461
Number of pages6
JournalEnergy Procedia
Publication statusPublished - Feb 2019
Externally publishedYes
Event10th International Conference on Applied Energy, ICAE 2018 - Hong Kong, China
Duration: 22 Aug 201825 Aug 2018

Bibliographical note

Publisher Copyright:
© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.


  • Aging trajectory prediction
  • Bayesian Monte Carlo
  • Lithium-ion batteries
  • Model migration
  • State-of-health


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