Among the most important tasks of a Battery Management System (BMS) are State of Charge (SoC) and State of Health (SoH) estimation. Many SoH estimation techniques are available, each with their advantages and drawbacks. These include methods based on a technique known as Electrochemical Impedance Spectroscopy (EIS). This technique provides detailed information about the battery's state of health but requires long rest times to prevent the battery relaxation effect from impacting the EIS measurement. In this paper EIS is shown to be able to track the short-term relaxation effect for batteries of different SoH. A SoH estimation method is proposed which combines fractional order impedance modeling and short-term relaxation effects with EIS characterization for rapid SoH determination. This empirical method is demonstrated to have an average SoH estimation error of less than 1%. As new methods arise to simplify EIS hardware requirements for real time applications, the proposed method offers a new way of utilizing EIS for SoH estimation.

电池管理系统 (BMS) 最重要的任务是充电状态 (SoC) 和健康状态 (SoH) 估计。有许多 SoH 估计技术可用，每种技术都有其优点和缺点。其中包括基于称为电化学阻抗谱 (EIS) 技术的方法。该技术提供有关电池健康状态的详细信息，但需要较长的休息时间以防止电池松弛效应影响 EIS 测量。在本文中，EIS 被证明能够跟踪不同 SoH 电池的短期松弛效应。提出了一种 SoH 估计方法，该方法将分数阶阻抗建模和短期松弛效应与 EIS 表征相结合，以快速确定 SoH。这种经验方法被证明具有小于 1% 的平均 SoH 估计误差。随着简化实时应用的 EIS 硬件要求的新方法的出现，所提出的方法提供了一种利用 EIS 进行 SoH 估计的新方法。