The assessment of battery health has long been a major concern in lithium-ion battery applications. Effective and efficient approaches for the estimation of state-of-health (SOH) are crucial to LiFePO 4 batteries with poor consistency. In this paper, we propose an estimation method based on Gaussian process regression (GPR) algorithm using partial charging curve. The curve is within a voltage range of only 60 mV, which is determined by a pre-analysis of voltage cumulative distribution. Considering the grade from grey relation analysis and the complexity to obtain, three interpretable health indicators are extracted from the charging voltage sequence as inputs to the model with a combined kernel function. The model achieves a root mean square error of 0.88% and outperforms several benchmark models on the tested public dataset. Furthermore, experimental results using data from the established platform reveal the high accuracy and adaptability of the proposed method.