Extract features from lithium-ion battery electrochemical impedance spectra and estimate state of health based on improved convolutional autoencoder-temporal convolutional network

Abstract

Predicting the state of health (SOH) of batteries is crucial for understanding their remaining lifespan and formulating more effective maintenance and management strategies. Utilizing electrochemical impedance spectroscopy (EIS) to assess the health status of batteries offers advantages such as high precision, rapid response, non-invasiveness, and reliability for accurately forecasting the remaining lifespan of batteries. In this paper, we propose an innovative method combining electrochemical impedance spectroscopy data to efficiently recognize and process complex patterns using deep neural networks by converting EIS into a two-dimensional image format. We have developed an improved convolutional autoencoder (ICAE) optimized to extract key features directly related to battery capacity in 2D EIS images and significantly improve feature characterization. The optimized features are further fed into the temporal convolutional network (TCN) to perform SOH prediction tasks. TCN utilizes its powerful time-dependent capture capability and long sequence memory mechanism to demonstrate superior performance in the field of SOH estimation. Compared with traditional methods, the proposed strategy not only significantly increases the prediction accuracy, but also opens up a new way to understand and analyze the internal relationship between complex time series and image data.