Electrochemical impedance spectroscopy (EIS) is a powerful diagnosis tool for the performance of electrochemical energy storage and conversion devices. One challenge for EIS-based diagnosis is the difficulty in separating highly overlapped impedance spectra. To overcoming this challenge, the distribution of relaxation times (DRT) method based on Tikhonov regularization algorithm can be employed. However, the accuracy and stability of the DRT deconvolution method not only depend on the penalty factor (the actual regularization tool), but also on the rarely studied shape factor (a potential regularization tool). In this study, a comprehensive investigation on the effect of the shape factor on both the accuracy and the stability of the DRT deconvolution method was conducted. First, the influence of the shape factor on the DRT deconvolution method was theoretically derived and numerically simulated. Second, the optimization methods for the shape factor and the selection of regularization matrices for DRT deconvolution were quantitatively evaluated using synthetic impedance data. Third, the coupling effect of the shape and penalty factors on the DRT deconvolution method was analyzed quantitatively. Finally, the collaborative optimization strategy was proposed and evaluated. The method presented in this paper can be used to improve the accuracy and reliability of the DRT to decode highly overlapped impedance spectra.

中文翻译：

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针对弛豫时间分布的形状因子优化，以更好地对电化学阻抗谱进行解卷积

电化学阻抗谱（EIS）是电化学能量存储和转换装置性能的强大诊断工具。基于 EIS 的诊断面临的一项挑战是难以分离高度重叠的阻抗谱。为了克服这一挑战，可以采用基于吉洪诺夫正则化算法的松弛时间分布（DRT）方法。然而，DRT反卷积方法的准确性和稳定性不仅取决于惩罚因子（实际的正则化工具），还取决于很少研究的形状因子（潜在的正则化工具）。本研究全面研究了形状因子对DRT反卷积方法的精度和稳定性的影响。首先，从理论上推导了形状因子对DRT反卷积方法的影响并进行了数值模拟。其次，使用合成阻抗数据对形状因子的优化方法和 DRT 反卷积正则化矩阵的选择进行了定量评估。第三，定量分析了形状因子和惩罚因子对DRT反卷积方法的耦合影响。最后提出协同优化策略并进行评估。本文提出的方法可用于提高 DRT 解码高度重叠阻抗谱的准确性和可靠性。