Future Ageing Trajectory Prediction for Lithium-Ion Battery Considering the Knee Point Effect

Kailong LIU*, Xiaopeng TANG*, Remus TEODORESCU, Furong GAO, Jinhao MENG

*Corresponding author for this work

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

38 Citations (Scopus)


Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available.

Original languageEnglish
Pages (from-to)1282-1291
Number of pages10
JournalIEEE Transactions on Energy Conversion
Issue number2
Early online date25 Nov 2021
Publication statusPublished - Jun 2022
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by High Value Manufacturing Catapult project under Grant 160080 CORE, in part by Fundamental Research Funds under Grant YJ202013, in part by Guangdong Scientific and Technological Project under Grant 2019A050516002, in part by Guangzhou Scientific and Technological Project under Grant 202002030323, in part by the Sichuan Science and Technology Program under Grant 2021YJ0063, and in part by China Postdoctoral Science Foundation under Grant 2020M673218.

Publisher Copyright:
© 1986-2012 IEEE.


  • Battery health
  • battery management
  • data-driven model
  • future ageing prediction
  • knee point
  • machine learning


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