State-of-Health Estimation for Lithium-Ion Batteries Using Domain Adversarial Transfer Learning

Abstract

Lithium-ion batteries are the main energy source of devices, and the estimation of their state-of-health (SOH) has become a hot point in prognostics and health management. However, many existing methods assume that training and testing data follow the same distribution. The model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution discrepancy. Thus, this article proposes a novel battery health prognostic model based on transfer learning. First, a novel transfer learning-based prognostic model, called deep domain adversarial network, is developed for SOH estimation of Lithium-ion batteries. Second, an unsupervised feature alignment metric is proposed, where maximum mean discrepancy and correlation alignment are considered simultaneously. Moreover, a generative adversarial learning is developed to guide the feature generator to provide the domain-invariant features. Finally, a novel feature generator, called dense bidirectional gated recurrent unit, is proposed to extract discriminate features from sensor signals. The effectiveness of DDAN for SOH estimation is verified on a battery dataset. The experimental results indicate that DDAN can effectively predict SOH of Lithium-ion battery, and significantly improve the performance of feature learning and knowledge transferring.