A Hybrid Battery Equivalent Circuit Model, Deep Learning, and Transfer Learning for Battery State Monitoring

Abstract

The accurate estimation of state of health (SOH) for lithium-ion batteries is significant to improve the reliability and safety of batteries in operation. However, many existing studies on battery SOH estimation are conducted on the premise of large sizable labeled training data acquisition without considering the time cost and experimental cost. To solve such issues, this article proposes a novel capacity prediction method for SOH estimation based on the battery equivalent circuit model (ECM), deep learning, and transfer learning. First, an actual charge–discharge experiment is carried out, and a simulation of the corresponding cycling process is conducted for virtual data acquisition using the battery equivalent model. Second, a convolutional neural network (CNN)-based feature extraction network is selected by conducting a performance comparison. Then, a capacity estimation model consisting of a feature extraction network, regressor, and feature alignment metric calculation modules is generated. Several transfer learning methods are chosen for feature alignment metric calculation. Finally, a capacity estimation performance comparison is done for the final selection of the feature alignment metric calculation methods. The results illustrate that the capacity prediction model established using virtual data and the generative adversarial network (GAN)-based transfer learning method has ideal prediction performance (with the 0.0941 of the maximum test error in all capacity estimation situation).