State of Health Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Reconstruction

To guarantee the safe and efficient operation of lithium-ion batteries, it is crucial to precisely estimate the state of health (SOH) of batteries. However, most of the existing studies have primarily focused on complete or large-range charging curves, which are highly challenging to acquire in practical applications. To this end, a novel SOH estimation method based on partial charging curve reconstruction is proposed in this paper. First, a partial charging curve reconstruction model based on a convolutional neural network (CNN) reconstructs the charging segments from voltage ranges with low SOH correlation to those with high SOH correlation. Second, health features (HFs) are extracted from the reconstructed charging segments and used as inputs to estimate the SOH based on a Gaussian process regression (GPR) model. Finally, validation experiments were conducted on two lithium-ion battery datasets to demonstrate the effectiveness and generalization of the proposed method. The proposed method enables precise SOH estimation using only a narrow charging segment (0.05 V), making it suitable for practical application scenarios.