Generative Adversarial Network for State of Health Estimation of Lithium-ion Batteries

Abstract

State of health (SOH) estimation is significant to predict the capacity of battery in the battery management systems. The most existing methods require sufficient labeled data to obtain the precise results. However, in the industrial application, it is difficult and costly to collect sufficient battery aging data. Thus, this paper proposed a generative model to tackle the data augmentation and SOH estimation of battery. Firstly, a conditional generative adversarial network is developed for data augmentation. Secondly, a hybrid feature generator, i.e., convolutional long short-term memory (CLSTM) is employed to reconstruct the real signals. Thirdly, a LSTM-based SOH estimator is employed to learn the degradation trance of the original and the artificially generated signals. Finally, a SOH estimation of battery testing is performed to verify the effectiveness of the proposed method. The experimental results indicate that the model can effectively implement data augmentation and SOH estimation of battery.