State of Health estimation method for lithium batteries based on electrochemical impedance spectroscopy and pseudo-image feature extraction

Abstract

Electrochemical impedance spectroscopy (EIS) is a key technique characterizing the batteries' State of Health (SOH). The extraction of features from the limited EIS information for SOH estimation relies heavily on the researcher's prior knowledge. This study proposes a method to enhance the EIS feature information and perform unsupervised feature extraction to estimate the SOH. First, the EIS data is transformed into images using Gramian angular field, which enhances the data features. Next, the images were subjected to unsupervised feature extraction using the VGG16 neural network framework. Finally, the unsupervised feature extraction and SOH prediction were integrated into a neural network framework to achieve end-to-end training and prediction. The experimental results show that the proposed method's SOH estimation error is less than 2%, and its accuracy is improved by 55.6% compared to its benchmark model; the feasibility of unsupervised feature extraction is demonstrated, overcoming the drawbacks of artificially performed feature extraction.