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

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.