Long short-term memory with attention mechanism for state of charge estimation of lithium-ion batteries

Tadele Mamo*, Fu Kwun Wang

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

36 Scopus citations

Abstract

Evaluating the state-of-charge of the battery's current cycle is one of the major tasks in the charge management of rechargeable batteries. We propose a long short-term memory model with an attention mechanism to estimate the charging status of two lithium-ion batteries. Data from three dynamic tests such as dynamic stress test, supplemental federal test procedure-driving schedule, and federal urban driving schedule are used to evaluate our model at different temperatures. One dataset or two datasets are used as the training data, and the other datasets are used as the test data. The model achieves the predictive root mean square errors of 0.9593, 0.8714, and 0.9216 at three different temperatures for the FUDS dataset. Moreover, the predictive RMSE of the proposed model is lower than 1.41 for all our experiments. We use the Monte Carlo dropout technique to verify the robust of the proposed model.

Original languageEnglish
Article number9096356
Pages (from-to)94140-94151
Number of pages12
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Attention mechanism
  • lithium-ion battery
  • long short-term memory
  • state-of-charge

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