Battery in-situ state-of-health (SOH) estimation has attracted considerable attention, but essentially, most studies focus on the normalized capacity or resistance, while the underlying aging mechanism is vacant. Therefore, we propose a voltage reconstruction model, which not only accurately estimates the SOH but also quantitatively identifies the aging modes. The model takes into consideration the limited battery operation range in practice and the over-potential caused by large current rate. Based on the matching relationship from the half-cell electrode equilibrium potentials to the full-cell terminal voltage, the complete terminal voltage is reconstructed via the partial charging data, and the SOH is estimated by a specific cut-off voltage range. We introduce matching differential voltage curves in the optimization objective of the model to further reduce the required data while achieving high accuracy.The model accuracy is investigated from the perspective of state-of-charge ranges and input data quality. The applicability is verified on various aging states and different cell designs. It is concluded that based on the limited data that contain one phase transition of the positive electrode and the negative electrode, the proposed model can estimate SOH with relative errors less than 2.5%, and quantify the potential aging modes, which holds promise for practical applications from a cell level to a pack level.

电池原位健康状态（SOH）估计引起了相当大的关注，但是从本质上讲，大多数研究集中在归一化容量或电阻上，而潜在的老化机制却空缺。因此，我们提出了一种电压重建模型，该模型不仅可以准确估算SOH，而且可以定量地识别老化模式。该模型考虑了实际中有限的电池工作范围以及由大电流率引起的过电势。基于从半电池电极平衡电势到全电池端电压的匹配关系，通过部分充电数据重建完整端电压，并通过特定的截止电压范围估算SOH。我们在模型的优化目标中引入了匹配的差分电压曲线，以在达到高精度的同时进一步减少所需数据。从荷电状态范围和输入数据质量的角度研究了模型精度。在各种老化状态和不同的电池设计上验证了其适用性。结论是，基于包含正电极和负电极的一个相变的有限数据，该模型可以估计相对误差小于2.5％的SOH，并量化潜在的老化模式，这为实际应用提供了希望从单元级别到包级别。在各种老化状态和不同的电池设计上验证了其适用性。结论是，基于包含正电极和负电极的一个相变的有限数据，该模型可以估计相对误差小于2.5％的SOH，并量化潜在的老化模式，这为实际应用提供了希望从单元级别到包级别。在各种老化状态和不同的电池设计上验证了其适用性。结论是，基于包含正电极和负电极的一个相变的有限数据，该模型可以估计相对误差小于2.5％的SOH，并量化潜在的老化模式，这为实际应用提供了希望从单元级别到包级别。