Deep domain adaptation network for transfer learning of state of charge estimation among batteries

Abstract

Machine learning-based methods have been extensively explored for state of charge (SoC) estimation of lithium-ion batteries. However, for transfer learning among different batteries, most methods focus on the fine tuning of network weights while the domain feature adaptation from source set to target set is not explicitly explored. Here, a Deep Domain Adaptation Network (DDAN) is proposed for SoC estimation with transfer purpose among different batteries. The proposed method incorporates domain adversarial mechanism and maximum mean discrepancy (MMD) to get domain invariant features between source and target datasets. It can be used for both supervised and unsupervised learning depending on the availability of target labels. Experimental results on different batteries show the effectiveness of the proposed method with reduced error to over half compared with the direct prediction baseline, which can be attributed to the intrinsic domain adaptation mechanism between source and target batteries. Our proposed method outperforms the state of the art methods and can be generalized to other batteries.