Model Migration Neural Network for Predicting Battery Aging Trajectories

Xiaopeng TANG, Kailong LIU*, Xin WANG, Furong GAO*, James MACRO, W. Dhammika WIDANAGE

*Corresponding author for this work

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

152 Citations (Scopus)

Abstract

An accurate prediction of batteries' future degradation is a key solution to relief the users' anxiety on battery lifespan and electric vehicles' driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this article, a feed-forward migration neural network (NN) is proposed to predict the batteries' aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging data set. This base model is then transformed by an input-output slope and bias correction (SBC) method structure to capture the degradation of target cell. To enhance the model's nonlinear transfer capability, the SBC model is further integrated into a four-layer NN and easily trained via the gradient correlation algorithm. The proposed migration NN is experimentally verified with four different commercial batteries. The predicted root-mean-square errors (RMSEs) are all lower than 2.5% when using only the first 30% of aging trajectories for NN training. In addition, the illustrative results demonstrate that a small-sized feed-forward NN (down to 1-5-5-1) is sufficient for battery aging trajectory prediction.

Original languageEnglish
Article number9028181
Pages (from-to)363-374
Number of pages12
JournalIEEE Transactions on Transportation Electrification
Volume6
Issue number2
Early online date9 Mar 2020
DOIs
Publication statusPublished - Jun 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Aging trajectory prediction
  • electric vehicle
  • lithium-ion battery management
  • model migration
  • neural network (NN)

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