LSTM-based estimation of lithium-ion battery SOH using data characteristics and spatio-temporal attention.

Autor:	Xu G; School of Engineering, Huzhou University, Huzhou, P. R. China., Xu J; School of Engineering, Huzhou University, Huzhou, P. R. China.; Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou, P. R. China.; Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, P. R. China., Zhu Y; School of Engineering, Huzhou University, Huzhou, P. R. China.
Jazyk:	angličtina
Zdroj:	PloS one [PLoS One] 2024 Dec 26; Vol. 19 (12), pp. e0312856. Date of Electronic Publication: 2024 Dec 26 (Print Publication: 2024).
DOI:	10.1371/journal.pone.0312856
Abstrakt:	As the primary power source for electric vehicles, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring the reliable operation of the power system. Long Short-Term Memory (LSTM), a special type of recurrent neural network, achieves sequence information estimation through a gating mechanism. However, traditional LSTM-based SOH estimation methods do not account for the fact that the degradation sequence of battery SOH exhibits trend-like nonlinearity and significant dynamic variations between samples. Therefore, this paper proposes an LSTM-based lithium-ion SOH estimation method incorporating data characteristics and spatio-temporal attention. First, considering the trend-like nonlinearity of the degradation sequence, which is initially gradual and then rapid, input features are filtered and divided into trend and non-trend features. Then, to address the significant dynamic variations between samples, especially for capacity regeneration,a spatio-temporal attention mechanism is designed to extract spatio-temporal features from multidimensional non-trend features. Subsequently, an LSTM model is built with trend features, spatio-temporal features, and actual capacity as inputs to estimate capacity. Finally, the model is trained and tested on different datasets. Experimental results demonstrate that the proposed method outperforms traditional methods in terms of SOH estimation accuracy and robustness. Competing Interests: The authors have declared that no competing interests exist. (Copyright: © 2024 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze:	MEDLINE
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