CAREER:Sensors for Quantification of Degradation in Polymer Electrolyte Fuel Cells

摘要：马修·曼奇研究所：宾夕法尼亚州立大学建议编号：0644811标题：职业：聚合物电解质燃料电池中降解的量化传感器许多复杂的系统逐渐退化，可能导致灾难性故障。由于退化的时间尺度相对较慢，用常规传感技术几乎不可能在早期阶段检测到这些类型的异常故障。PI正在规划一种方法，以便能够及早检测和量化聚合物电解质燃料电池(PEFC)中潜在的灾难性不断演变的故障。目标是在实施异常缓解和正常降级策略之前进行早期检测，从而扩展服务可用性、平均性能，并实现更积极的初始设计。这一目标对于实现目前限制PEFC的延长寿命至关重要。实现这种感应能力的方法类似于心电图，在心电图中，心脏对外部应激的反应的时间序列数据被用来快速诊断经过多年演变的疾病。这种感应和量化方法通过快速时间尺度刺激-反应方法提供对相对缓慢变化的异常的早期诊断。在选择外部施加的刺激之后，使用符号动力学将时间序列数据分割成响应向量。将动态响应向量特征与标称响应向量进行比较，以精确量化异常程度。这种早期故障检测技术将使几乎难以察觉的慢时间尺度异常能够在快速时间尺度上被在线量化，远远早于显著的退化发生。广泛影响：这项研究的更广泛影响应该有助于生成实现无处不在的PEFC实施所需的基础设施和知识库，并减少美国对化石燃料的长期依赖。在Nuvera和技术开发公司TA&amp；D的合作下，CO传感器将被开发成原型，以最大限度地发挥影响。在教育方面，该项目将让数百名学生，从高中到研究生，接触燃料电池技术。三名博士生将直接参与，每年40名本科生将参加升级的高级课程，包括工业演讲者和与亚利桑那大学的合作。目前正在编写的一本教科书也将受益于这项研究的成果。高中生和教师将参加一个暑期项目，并将开发一门新的研究生课程。总体而言，综合研究和教育计划将有助于建立一个永久性的基础设施，以应对目前阻碍PEFC广泛采用的关键技术挑战。

ABSTRACTPI: Matthew Mench Institution: Pennsylvania State UniversityProposal Number: 0644811Title: CAREER: Sensors for Quantification of Degradation in Polymer Electrolyte Fuel CellsIntellectual meritMany complex systems suffer gradual degradation that can result in catastrophic failure. Because the time scale of degradation is relatively slow, these types of anomalous faults are nearly impossible to detect at an early stage with conventional sensing technology. The PI is planning an approach that is to enable early detection and quantification of potentially catastrophic evolving faults in polymer electrolyte fuel cells (PEFCs). The objective is early detection that will precede implementation of anomaly mitigation and graceful degradation strategies, extending service availability, mean performance, and enabling a more aggressive initial design. This objective is essential to achieve the extended longevity that currently limits PEFCs. The methodology to achieve this sensing capability is heuristically similar to an electrocardiogram, in which the time series data of a heart's response to external stress are used to rapidly diagnose ailments that have evolved over many years.This sensing and quantification methodology provides early stage diagnosis of relatively slowly varying anomalies through a fast time scale stimulus-response approach. Following a chosen externally applied stimulus, the time series data are partitioned into a response vector using symbolic dynamics. The dynamic response vector characteristics are compared to a nominal response vector to precisely quantify the anomaly level. This early fault detection technique will enable nearly imperceptible slow time scale anomalies to be quantified online in a fast time scale, well before significant degradation occurs.Broader Impact:The broader impact of this research should help generate the infrastructure and knowledge base needed to enable ubiquitous PEFC implementation, and reduce long term United States dependence of fossil fuels. The CO sensor will be developed into a prototype under a collaboration between Nuvera and TA&D, a technology development company, maximizing impact. Educationally, the program will expose hundreds of students, from high school through graduate school, to fuel cell technology. Three Ph.D. students will be directly involved, and forty undergraduates per year will be enrolled in an upgraded senior level course including industrial speakers and a collaboration with the University of Arizona. A textbook currently in development will also benefit from the output of this research. High school students and teachers will participate in an summer program, and a new graduate-level course will be developed. Overall, the integrated research and educational program will help establish a permanent infrastructure to address key technical challenges that presently hinder widespread PEFC adoption.