Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy

The advancement of consumer electronics and electric vehicles requires heavy use of energy sources, particularly in the form of rechargeable batteries. Although lithium-ion batteries (LiBs) enable the use of such technologies owing to their high energy and power densities, estimating the state-of-health (SOH) of such batteries remains a challenge because of the various environmental operational conditions that affect the charging and discharging cycles of LiBs. In this study, we explore an approach that uses a convolutional autoencoder (CAE) for overcomplete feature extraction from electrochemical impedance spectroscopy (EIS) data. Subsequently, the extracted latent data representation is fed into a deep neural network (DNN) for battery capacity retention and SOH estimation. The proposed end-to-end deep learning-based architecture is called CAE-DNN. To prove the effectiveness of the proposed architecture, we conducted a series of experiments using a public dataset involving EIS spectra collected from fully charged LiBs cycled at different temperatures. The experimental results were compared with those of existing state-of-the-art methods, and with other classic machine learning methods. The results demonstrate that the proposed architecture extracts useful features in an unsupervised manner and estimates the SOH of LiBs more accurately than other baseline estimation methods.