Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Xiaopeng TANG, Kailong LIU*, Xin WANG, Boyang LIU, Furong GAO, Widanalage Dhammika WIDANAGE

*Corresponding author for this work

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

55 Citations (Scopus)

Abstract

Predicting batteries' future degradation is essential for developing durable electric vehicles. The technical challenges arise from the absence of full battery degradation model and the inevitable local aging fluctuations in the uncontrolled environments. This paper proposes a base model-oriented gradient-correction particle filter (GC-PF) to predict aging trajectories of Lithium-ion batteries. Specifically, under the framework of typical particle filter, a gradient corrector is employed for each particle, resulting in the evolution of particle could follow the direction of gradient descent. This gradient corrector is also regulated by a base model. In this way, global information suggested by the base model is fully utilized, and the algorithm's sensitivity could be reduced accordingly. Further, according to the prediction deviations of base model, weighting factors between the local observations and base model can be updated adaptively. Four different battery datasets are used to extensively verify the proposed algorithm. Quantitatively, the RMSEs of GC-PF can be limited to 1.75%, which is 44% smaller than that of the conventional particle filter. In addition, the consistency of predictions when using different size of training data is also improved by 32%. Due to the pure data-driven nature, the proposed algorithm can also be extendable to other battery types.

Original languageEnglish
Article number227118
Number of pages11
JournalJournal of Power Sources
Volume440
DOIs
Publication statusPublished - 15 Nov 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work was financially supported by National Natural Science Foundation of China project ( 61433005 ), Hong Kong Research Grant Council ( 16207717 ), Guangdong Scientific and Technological Project ( 2017B010120002 ) and the European Union Horizon 2020 research and innovation programme ( 685716 ).

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

  • Aging trajectory prediction
  • Bayesian Monte Carlo
  • Electric vehicle
  • Gradient correction
  • Lithium-ion battery management

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