This paper proposes an advanced state of health (SoH) estimation method for high energy NMC lithium-ion batteries based on the incremental capacity (IC) analysis. IC curves are used due to their ability of detect and quantify battery degradation mechanism. A simple and robust smoothing method is proposed based on Gaussian filter to reduce the noise on IC curves, the signatures associated with battery ageing can therefore be accurately identified. A linear regression relationship is found between the battery capacity with the positions of features of interest (FOIs) on IC curves. Results show that the developed SoH estimation function from one single battery cell is able to evaluate the SoH of other batteries cycled under different cycling depth with less than 2.5% maximum errors, which proves the robustness of the proposed method on SoH estimation. With this technique, partial charging voltage curves can be used for SoH estimation and the testing time can be therefore largely reduced. This method shows great potential to be applied in reality, as it only requires static charging curves and can be easily implemented in battery management system (BMS).

本文提出了一种基于增量容量（IC）分析的高级高能NMC锂离子电池健康状态（SoH）估算方法。使用IC曲线是因为它们具有检测和量化电池退化机制的能力。提出了一种基于高斯滤波器的简单而鲁棒的平滑方法，以减少IC曲线上的噪声，因此可以准确识别与电池老化有关的信号。在电池容量与IC曲线上感兴趣特征（FOI）的位置之间发现线性回归关系。结果表明，通过一个单电池组开发的SoH估计函数能够评估在不同循环深度下循环的其他电池的SoH，最大误差小于2.5％，证明了该方法在SoH估计方面的鲁棒性。利用这种技术，可以将部分充电电压曲线用于SoH估算，因此可以大大减少测试时间。这种方法具有巨大的潜力，因为它仅需要静态充电曲线，并且可以轻松地在电池管理系统（BMS）中实施，因此具有很大的应用潜力。