A novel unsupervised domain adaptation-based method for lithium-ion batteries state of health prognostic

Abstract

Precise state-of-health prognostic is crucial for the safe and reliable operation of Lithium-ion batteries in energy storage systems. Nevertheless, most data-driven state-of-health prognostic methods are only effective when the distribution of laboratory training data and test data is consistent. This paper proposes an unsupervised domain adaptation method for individualized SOH estimation. More specifically, a convolutional neural network is used to automatically extract battery aging features from raw charging voltage curves. Besides, we define a geometrical distance between different domain feature subspaces based on the principal angles and learn deep transferable features by minimizing it. In this way, we can narrow the domain gap by using orthogonal bases of the feature spaces, while avoiding changes in feature scales. To maintain the geometrical properties of feature subspaces to the maximum extent possible, we further apply the bases mismatch penalization to penalize the matching of orthogonal bases of different importance. Our method is evaluated on three different datasets with different chemistries, profiles, and ambient temperatures to validate the effectiveness and generalizability. Our method outperforms several other domain adaptation approaches significantly. The results show that the proposed transfer learning-based method is widely applicable and can greatly shorten the duration of battery aging experiments.