State of health estimation of lithium-ion battery based on DAGRU

Jiangnan Hong; Wu Wang; Qinqin Chai; Qiongbin Lin; Fenghuang Cai

doi:10.1117/12.2684768

19 October 2023 State of health estimation of lithium-ion battery based on DAGRU

Jiangnan Hong, Wu Wang, Qinqin Chai, Qiongbin Lin, Fenghuang Cai

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092D (2023) https://doi.org/10.1117/12.2684768
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Accurate estimation of the state of health (SOH) of Li-ion batteries is an important guarantee to ensure safe and reliable operation of battery systems, and is also a key indicator for battery management systems. In this paper, a method for SOH estimation of lithium-ion batteries based on double-stage attention-based gated recurrent unit (DAGRU) is proposed. Firstly, depth wise separable convolution (DSC) was used to extract the health features (HFs) of battery charging voltage. Secondly, the model uses double-stage attention, which enables more information on input features to be obtained at both the temporal and spatial scales. Finally, NASA battery dataset was used to verify the proposed method. Experimental results show that the proposed method can accurately estimate the SOH of lithium-ion batteries.

Citation Download Citation

Jiangnan Hong, Wu Wang, Qinqin Chai, Qiongbin Lin, and Fenghuang Cai "State of health estimation of lithium-ion battery based on DAGRU", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092D (19 October 2023); https://doi.org/10.1117/12.2684768

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