Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy

Machine learning-based methods have emerged as a promising solution to accurate battery capacity esti-mation for battery management systems. However, they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities, leading to pro-hibitive costs and efforts for data collection. In response to this issue, this study proposes a convolutional neural network (CNN) based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input. More importantly, an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process, thereby significantly alleviating the cost of collecting training data. Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method. The results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are given. However, the estimation performance of supervised machine learning algorithms sharply deteri-orates when fewer samples with measured capacities are available. In this case, the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%. A fur-ther validation under different current rates and states of charge confirms the effectiveness of the pro-posed method. Our method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks. (c) 2022 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights reserved.