A Dynamic Spatial-Temporal Attention-Based GRU Model with Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

Shengmin Cui, Inwhee Joe

Research output: Contribution to journalArticlepeer-review

Abstract

A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-Temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery's charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models.

Original languageEnglish
Article number9350252
Pages (from-to)27374-27388
Number of pages15
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Lithium-ion battery
  • dynamic spatial attention
  • gated recurrent unit
  • state of health
  • temporal attention

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