Unsupervised feature extraction for lithium-ion battery electrochemical impedance spectroscopy and capacity estimation using deep learning method

Abstract

In this paper, we propose an unsupervised feature extraction technique using a variational autoencoder and generative adversarial network (VAEGAN) that automatically extracts meaningful latent variables from the electrochemical impedance spectra (EIS) of lithium-ion (Li-ion) batteries. These variables accurately reflect the degree of Li-ion battery degradation and are closely related to its capacity. By analyzing the latent variables, it was found that the network can learn the effect of Li-ion battery capacity degradation on the EIS. The extracted latent variables are then used as input features for Gaussian Process Regression (GPR) to estimate the capacity of Li-ion batteries under uneven usage and with unknown historical data. The results show that the capacity prediction method proposed in this paper can significantly reduce prediction errors compared to the current state-of-the-art methods. The method not only provides a more reliable capacity estimation but is also robust to highly noisy EIS data, making it more suitable for practical application scenarios.