发表机构
Jožef Stefan Institute; Jožef Stefan International Postgraduate School; Graz University of Technology; Faculty of Information Studies in Novo mesto（约热·斯特凡研究所; 约热·斯特凡国际研究生学院; 格拉茨工业大学; 新梅斯托信息研究学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对从EIS估计DRT的不适定逆问题，提出物理感知卷积自编码器，可解析重叠弛豫过程、重构多数据集测量值，还能通过隐空间监测电池状态变化与退化。
AI 中文摘要
从电化学阻抗谱（EIS）估计弛豫时间分布（DRT）是一个不适定逆问题，对正则化选择高度敏感。我们提出一种物理感知卷积自编码器，可直接从EIS数据估计DRT，无需针对特定频谱进行调优。训练过程中嵌入阻抗与DRT之间的离散关系，约束网络生成与阻抗一致的分布。该模型可解析合成双ZARC频谱中的重叠弛豫过程，并准确重构来自三个独立固体氧化物燃料电池与电解池数据集的测量值，范围归一化误差低于1.1%。解码器探针分析显示，学习到的隐表示按弛豫时间尺度组织，隐空间中的距离可捕捉运行状态变化、氢短缺事件及长期退化。同一轻量架构无需修改即可应用于所有数据集，提供一致的DRT估计及可解释的状态监测基础。
英文摘要
Estimating the distribution of relaxation times (DRT) fromelectrochemical impedance spectroscopy (EIS) is an ill-posed inverse problem that is highly sensitive to regularisation choices. We propose a physics-informed convolutional autoencoder that estimates DRT directly from EIS data without spectrum-specific tuning. A discretised relation between impedance and the DRT is embedded in the training process, constraining the network to produce impedance-consistent distributions. The model resolves overlapping relaxation processes in synthetic two-ZARC spectra and accurately reconstructs measurements from three independent solid oxide fuel and electrolysis cell datasets, with range-normalised errors below 1.1%. Decoder-probe analysis shows that the learned latent representation is organised according to relaxation timescale. Distances in this latent space capture operating changes, hydrogen-shortage events, and long-term degradation. The same lightweight architecture is applied across all datasets without modification, providing consistent DRT estimation and an interpretable basis for condition monitoring.
