A novel health indicator by dominant invariant subspace on Grassmann manifold for state of health assessment of lithium-ion battery

Abstract

The precise estimation of the state of health (SoH) in Lithium-ion batteries (LiBs) relies heavily on a reliable health indicator (HI). Conventional indicators are often constructed by directly concatenating features from multiple sources. It overlooks significant non-linear and correlative information inherent in raw signals. To address this limitation, this paper introduces an innovative approach for SoH estimation in LiBs. Deep features extracted from signals of various sensors are obtained using denoising auto-encoders (DAEs). Then the dominant invariant subspaces (DIS) are calculated through the non-linear transformation of multi-source features on the Grassmann manifold. It can preserve essential and robust characteristics. The health indicator quantifies the geodesic distance of DIS using a projection metric. It provides a more comprehensive inclusion of nonlinear and correlation information. Consequently, this indicator offers heightened precision in discerning differences in health states. Validation of the proposed method is conducted using the NASA dataset. The result demonstrates its effectiveness on the SoH assessment and superiority to the state-of-the-art method.