Zuo Jian, Lv Hong, Zhou Daming, Xue Qiong, Jin Liming, Zhou Wei, Yang Daijun, Zhang Cunman
Clean Energy Automotive Engineering Center and School of Automotive Studies, Tongji University, Shanghai 201804, China.
School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, PR China.
Data Brief. 2021 Jan 18;35:106775. doi: 10.1016/j.dib.2021.106775. eCollection 2021 Apr.
This dataset collects the long-term dynamic durability test data and the polarization characterization test data used in our research article [1]. The dynamic durability test and the polarization characterization test of a single proton exchange membrane fuel cell (PEMFC) are all performed on the Greenlight 20 test station. The European harmonized test protocol is adapted to construct the fuel cell dynamic load test cycle (FC-DLC) used in this work. The overall durability test is composed of 3076 FC-DLC cycles, around 1008 h. To access the degradation information of the test fuel cell, the polarization characterization tests are performed periodically during the durability test. In this work, the characterizations were performed at time: 0, 100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 h. During the test period, G20 test station records all measured data, includes the dynamic load durability test dataset and the polarization test dataset. The output voltage degradation trend as well as the polarization curves are plotted and described in this work. This dataset provides the possibilities to study the degradation phenomenon of fuel cell operating by dynamic load cycles, moreover, this dataset can be directly used to various prediction models build for fuel cells.
该数据集收集了我们研究论文[1]中使用的长期动态耐久性测试数据和极化特性测试数据。单个质子交换膜燃料电池(PEMFC)的动态耐久性测试和极化特性测试均在Greenlight 20测试站上进行。采用欧洲统一测试协议构建了本工作中使用的燃料电池动态负载测试循环(FC-DLC)。整体耐久性测试由3076个FC-DLC循环组成,约1008小时。为了获取测试燃料电池的降解信息,在耐久性测试期间定期进行极化特性测试。在本工作中,表征在以下时间点进行:0、100、200、300、400、500、600、700、800、900和1000小时。在测试期间,G20测试站记录所有测量数据,包括动态负载耐久性测试数据集和极化测试数据集。本工作绘制并描述了输出电压降解趋势以及极化曲线。该数据集为研究燃料电池在动态负载循环下的降解现象提供了可能性,此外,该数据集可直接用于构建各种燃料电池预测模型。