Selective Domain Adaptation Network for Lithium-ion Battery Health Monitoring

Abstract

Lithium-ion battery health monitoring is crucial in ensuring the reliability of the power system. Due to complex and dynamic battery operating conditions (e.g., ambient temperature and discharge current), domain shift is an ineluctable issue in battery health monitoring. In this study, a novel transfer learning (TL) method, i.e., selective domain adaptation network (SDANet) is developed for solving the problem of domain shift and performing battery health monitoring. Firstly, an unsupervised domain selection mechanism is established to select the optimal source domain, so as to minimize negative transfer in TL. Then, an adaptive feature transmission mechanism (AFTM) is proposed to improve gradient propagation and the performance of feature learning. Thirdly, the selective domain adaptation method is carried out according to channel similarity, which effectively solves the problem of domain shift and improves the performance of battery health estimation. The experiment results demonstrate that SDANet has excellent battery health monitoring performance under various working conditions.