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使用Loewner框架进行电化学阻抗光谱的数据驱动分析

Bansidhar Patel¹, Antonio Sorrentino¹, Tanja Vidakovic-Koch¹

¹Max Planck Institute for Dynamics of Complex Technical Systems, Electrochemical Energy Conversion, Sandtorstrasse 1, D-39106 Magdeburg, Germany.

|March 17, 2025

概括

本研究引入了一种数据驱动的方法,使用Loewner框架来识别电化学阻抗光谱数据的最佳等效电路模型,改进电极-电解质接口的分析.

科学领域:

电化学电化学电化学
材料科学材料科学材料科学
数据科学数据科学数据科学

背景情况:

优化电化学设备需要了解电极-电解质接口的质量传输和电动力学.
电化学阻抗光谱 (EIS) 对于探测这些过程至关重要,通常用等效电路模型 (ECM) 进行分析.
由于不同模型之间的光谱相似性,选择正确的ECM是很困难的,这阻碍了准确的物理表示.

研究的目的:

提出一种基于数据的方法,使用Loewner框架 (LF) 提取放松时间 (DRT) 的分布.
为了方便确定最适合给定EIS数据集的ECM.
在常见的Randles ECM上验证该方法,并通过噪音数据评估其性能.

主要方法:

使用Loewner框架 (LF) 来以数据驱动的方式提取放松时间 (DRT) 的分布.
将该方法应用于电化学阻抗光谱 (EIS) 数据集.
使用Randles等效电路模型 (ECM) 的变体验证了该方法.

主要成果:

洛恩纳框架 (LF) 成功地从EIS数据中提取了放松时间 (DRT) 的分布.
该方法有助于确定电化学接口最合适的等效电路模型 (ECM).
这种方法在杂的数据集中表现出稳健性,并且比传统的反转算法具有优势.

结论:

通过LF进行数据驱动的DRT提取提供了一种可靠的方法,用于在电化学分析中选择合适的ECM.
这种技术提高了从EIS数据的物理解释的准确性.
基于LF的方法为现有的ECM识别技术提供了强大的和有利的替代方案.

关键词:

电化学电化学电化学物理学中的数学方法.

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