A novel state-of-health estimation method for fast charging lithium-ion batteries based on an adversarial encoder network

Abstract

Deep learning based on big data has been widely implemented with cloud and edge computing for lithium-ion battery online health diagnostics, where enhancing the diagnostics' accuracy, robustness, and real-time application are current research issues. Most research has concentrated on state-of-health estimation methods for diagnostics, but the difference in the degradation feature trajectories of batteries between the training and testing domains has been neglected, which further affects the estimation precision of the trained model. To overcome this issue, a strategy based on adversarial learning and feature selection is proposed. First, the adversarial encoder approach creates a lower-dimensional feature vector to the aggregated domain between the source and target batteries. Then, the feature vector is sent into the estimation module, which consists of a one-dimensional convolutional neural network (1DCNN) to accurately estimate the battery lifespan. The results on the 1C and 6C experimental datasets demonstrate that the adversarial feature selection strategy significantly enhances the estimation performance of the deep learning-based estimation method. Combining an adversarial autoencoder with the prediction module yields statistically superior battery lifetime estimation results.