Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

Xiaopeng TANG, Changfu ZOU*, Ke YAO, Jingyi LU, Yongxiao XIA, Furong GAO*

*Corresponding author for this work

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

93 Citations (Scopus)


This paper develops a new prediction method for the aging trajectory of lithium-ion batteries with significantly reduced experimental tests. This method is driven by data collected from two types of battery operation modes. The first type is accelerated aging tests that are performed under stress factors, such as overcharging, over-discharging and large current rates, and cover most of the battery lifespan. In the second operation mode, the same kinds of cells are aged at normal speeds to generate a partial aging profile. An accelerated aging model is developed based on the first type of data and is then migrated as a new model to describe the normal-speed aging behavior. Under the framework of Bayesian Monte Carlo algorithms, the new model is parameterized based on the second type of data and is used for prediction of the remaining battery aging trajectory. The proposed prediction method is validated on three types of commercial batteries and also compared with two benchmark algorithms. The sensitivity of results to the number of cycles is investigated for both modes. Illustrative results demonstrate that based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories (up to 500 cycles) at a root-mean-square error of less than 2.5% for all considered scenarios. When only using the first five-cycle data for model training, such a prediction error is bounded by 5% for aging trajectories of all the tested batteries.

Original languageEnglish
Article number113591
JournalApplied Energy
Publication statusPublished - 15 Nov 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported in part by the National Natural Science Foundation of China project ( 61227005 ), Hong Kong Research Grant Council ( 16233316 and 16207717 ) and Guangdong scientific and technological project ( 2017B010120002 ).

Publisher Copyright:
© 2019 Elsevier Ltd


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


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