Dataset of "Electrochemical Signatures in Proton Exchange Membrane Fuel Cells: A Comprehensive Study Based on Distribution of Relaxation Times"

The Distribution of Relaxation Times (DRT) method is increasingly applied to electrochemical impedance spectroscopy (EIS) for polymer electrolyte membrane fuel cells (PEMFCs), yet peak interpretation remains challenging due to overlapping processes and sensitivity to operating conditions. In this work, impedance spectra were measured on a well-defined single PEMFC across a broad experimental matrix, including variations in voltage, temperature, pressure, gas stoichiometry, and oxidant type. The novelty of this study lies not in the identification of peaks themselves, but in the systematic quantification of how their relative contributions evolve under such diverse conditions. A non-linear statistical framework was employed to reveal correlations between peaks and operating parameters, thereby elucidating the highly non-linear mechanistic interplay underlying PEMFC operation. This approach also enables assessment of the statistical significance of peaks, distinguishing genuine electrochemical features from phantom peaks, while the presentation of the average DRT reconstruction error with confidence intervals highlights non-negligible uncertainties in the high-frequency region.Dataset collected by Francesco Mazzeo during his research period abroad at the University of Chemistry and Technology Prague as part of his PhD program at Politecnico di Torino, carried out within the PNRR-NGEU project, which received funding from the MUR – DM 352/2022, as well as from Eaton.弛豫时间分布法（Distribution of Relaxation Times, DRT）正日益被应用于质子交换膜燃料电池（Polymer Electrolyte Membrane Fuel Cells, PEMFCs）的电化学阻抗谱（Electrochemical Impedance Spectroscopy, EIS）分析，但由于多过程峰重叠以及对运行工况的敏感性，峰解析仍颇具挑战。本研究针对一台参数明确的单台质子交换膜燃料电池，在宽泛的实验变量矩阵下开展阻抗谱测量，涵盖电压、温度、压力、气体化学计量比及氧化剂类型等多种工况变量。本研究的创新点并非在于峰的识别本身，而是系统量化了在上述多样运行条件下各峰相对贡献的演化规律。研究采用非线性统计框架，揭示了峰特征与运行参数之间的关联，进而阐明了质子交换膜燃料电池运行背后高度非线性的机制交互作用。该方法还可实现峰的统计学显著性评估，区分真实电化学特征与伪影峰；同时，带有置信区间的平均DRT重构误差结果，凸显了高频区域存在不可忽视的不确定性。本数据集由Francesco Mazzeo在都灵理工大学（Politecnico di Torino）攻读博士学位期间，于捷克布拉格化工大学（University of Chemistry and Technology Prague）访学阶段采集完成。本研究依托PNRR-NGEU项目开展，该项目获得了意大利大学与研究部（MUR）DM 352/2022以及伊顿（Eaton）的资助。