Health condition monitoring of lithium-ion batteries plays a crucial role in guaranteeing the reliability and safety of energy storage system. However, it is difficult to directly measure state of health of cell, which is sensitive to the complex application scenarios and related to battery internal physicochemical characters. This paper proposes a remaining capacity prediction technique for lithium-ion batteries based on partial charging curve and health feature fusion. Here, health features are extracted from partial charging profile and the fused health feature is obtained via canonical correlation analysis, which is utilized to improve feature correlation and reduce data dimension. A battery aging model is established by Gaussian process regression and validation is conducted on six battery data sets from National Aeronautics and Space Administration (NASA) and University of Oxford. The results show that the proposed method can achieve reliable and accurate battery capacity estimation.

锂离子电池的健康状态监测对于保障储能系统的可靠性和安全性起着至关重要的作用。然而，对于复杂的应用场景敏感且与电池内部理化特性有关，很难直接测量电池的健康状态。本文提出了一种基于部分充电曲线和健康特征融合的锂离子电池剩余容量预测技术。在这里，从部分充电轮廓中提取健康特征，并通过典型相关分析获得融合的健康特征，用于提高特征相关性并降低数据维度。通过高斯过程回归建立电池老化模型，并在来自美国国家航空航天局（NASA）和牛津大学的六个电池数据集上进行验证。结果表明，所提出的方法能够实现可靠、准确的电池容量估计。