FGNet: Feature Engineering-Guided Attentive Graph Neural Network for SOH Estimation of Lithium Battery

Abstract

The precise estimation of the state-of-health (SOH) in lithium-ion battery (LIB) holds significant importance for ensuring the safe operation of electric vehicles (EVs). While existing methods predominantly center around conventional recurrent neural networks (RNNs), these approaches inherently encounter challenges in effectively extracting features and modeling ultra-long-term time-series data. In this article, we introduce a novel time-series prediction framework that integrates feature engineering with an attentive graph neural network (GNN) to enhance the accuracy and efficiency of SOH estimation for lithium batteries. Specifically, we begin by extracting health factors (HFs) from the original charge and discharge data of the battery. Subsequently, we identify 3-D features exhibiting strong correlations, which serve as the primary inputs for the network. Our proposed network, termed the feature engineering-guided attentive graph neural network (FGNet), is designed to comprehensively capture and model the intricate relationships between HFs and SOH output. This is achieved by embedding both the feature extraction module and the attention module within the GNN architecture. Extensive experiments are conducted using the Center for Advanced Life Cycle Engineering (CALCE) aging dataset and National Aeronautics and Space Administration (NASA) battery dataset. The results substantiate the superiority of our method over existing state-of-the-art methods, with a mean absolute error (MAE) consistently averaging below 2%.