期刊
IEEE ACCESS
卷 9, 期 -, 页码 27374-27388出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3058018
关键词
Batteries; Estimation; Feature extraction; Integrated circuit modeling; Aging; Mathematical model; Lithium-ion batteries; Lithium-ion battery; state of health; dynamic spatial attention; temporal attention; gated recurrent unit
资金
- Institute for Information and Communications Technology Promotion (IITP) - Korea government (MSIP) [2020-0-00107]
The paper proposes a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model for accurately estimating the State-of-Health of Lithium-ion batteries, with experiments showing that the model achieves higher accuracy than conventional models.
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.
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