Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation

Abstract

State-of-health (SOH) estimation of batteries is crucial for ensuring the safety of energy storage systems. Prediction models based on external information (current, voltage, etc.) and artificial neural networks (ANN) are effective solutions. However, external information easily interferes, and the ANN-based model has data dependence, high energy consumption, and insufficient cognitive ability. This motivates us to utilize precise battery physical and chemical degradation information and brain-inspired spiking neural networks (SNNs) for accurate SOH estimation. Therefore, this study proposes a bioinspired spiking spatiotemporal attention neural network (SSA-Net) framework for battery health state monitoring by utilizing full-life-cycle electrochemical impedance spectroscopy (EIS). SSA-Net perfectly models brain neurons' information transmission mechanism and neuron dynamics, thereby endowing it with efficient spatiotemporal feature processing capabilities and low power consumption. Based on the designed spiking residual architecture, SSA-Net constructs a deep spiking information encoding framework achieving high gradient transfer efficiency. More importantly, this study proposes a novel SNN-based spiking spatiotemporal attention module, which realizes the enhancement of useful spiking features and discards worthless information through an adaptive spiking feature selection mechanism. Experimental results show that SSA-Net effectively extracts electrochemical features associated with battery degradation, facilitating precise modeling of the nonlinear relationship between EIS data and SOH and achieving competitive performance.