Semi-supervised self-learning-based lifetime prediction for batteries

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24 Citations (Scopus)


Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This article proposes a novel semi-supervised self-learning method for battery lifetime prediction. First, three health indicators (HIs) are extracted from the partial capacity-voltage curve. Second, the capacity estimation model and lifetime prediction model are built using data from three randomly selected batteries in the source domain. Then, the HIs are used to reconstruct the historical capacities to provide pseudo values for self-training of the lifetime model. Finally, the self-trained lifetime model is used to predict future degradation. The uncertainty expression is also included to provide the probabilistic prediction of future capacities. Different application scenarios are considered in the verification. The mean lifetime prediction error is less than 23 cycles with only three known checkpoints for batteries aging under different profiles. Predictions for different battery types show that the errors are less than 50 cycles with relative errors less than 4.1% for long lifespan batteries, and less than 20 cycles with relative errors less than 5.21% for short lifespan batteries. This article guides proper solutions for lifetime prediction when the labeled capacities in the real world are limited.

Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Issue number5
Pages (from-to)6471-6481
Number of pages11
Publication statusPublished - 1 May 2023

Bibliographical note

Publisher Copyright:


  • Aging
  • Artificial neural networks
  • Batteries
  • Data models
  • Degradation
  • Estimation
  • Lithium-ion battery
  • Predictive models
  • degradation reconstruction
  • lifetime prediction
  • probabilistic prediction
  • semi-supervised learning
  • semisupervised learning
  • Degradation reconstruction
  • lithium-ion battery


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