Entwicklung eines fahrzeugbasierten präskriptiven Lebensdauermanagements zur degradationstoleranten Betriebsführung von PEM-Brennstoffzellensystemen

Green hydrogen is the key to a sustainable transformation of the industry and transport sector. Nevertheless, various factors inhibit the market penetration of hydrogen-based mobility. For fuel cell vehicles, complex interactions between cost, performance and durability in particular pose a challenge. For this purpose, various optimization strategies are being developed along the life cycle of a fuel cell electric vehicle to reduce both cost and degradation while maintaining acceptable performance and efficiency through precise material selection and pairing, robust design, and intelligent operating and maintenance strategies. With the evolutionary trend of fuel cell systems for passenger cars to heavy-duty vehicle applications, the focus is shifting from capital cost to total cost of ownership. This allows the use of more expensive materials and system components, but at the same time places higher demands on the fuel cell system in terms of lifetime and minimum system power at the end of life. The high complexity of degradation mechanisms in fuel cell systems continuously complicates the development of reliable mathematical degradation models with an adequate computational effort for the implementation of a robust fuel cell system design and operation strategy. In this work, an innovative concept for degradation-tolerant control of fuel cell systems is developed with the vehicle-based prescriptive lifetime management and investigated in different scenarios. The concept is based on on-board operando feature extraction and condition assessment of effective physical structural parameters as well as a hybrid approach for remaining useful life prediction. A risk assessment of the fuel cell system components first shows that especially the proton exchange membrane and the cathode catalyst layer within the fuel cell stack have a high risk of premature failure and need to be monitored on-board. Using commercially available DC and AC based measurement technology, condition indicators of the proton exchange membrane and the cathode catalyst layer can be extracted during operation. Vehicle-based condition monitoring of the fuel cell stack components is thereby enabled by correlating the extracted condition indicators with physical structural parameters. A fusion of data-driven and model-based lifetime prediction methods subsequently allows the prediction of the remaining useful life. Finally, the condition assessment and lifetime prediction serve as a starting point for a reconfiguration of the operating strategy through improving the operating parameters based on an optimization-based post-diagnostic or post-prognostic decision-making

Analyse von zukünftigen Urban Air Mobility Stationsnetzwerken mit k-means Clustering

Urban transportation is facing new challenges due to the growing population in urban areas and the associated increase in commuter traffic. One way to ease the strain on ground-based transportation is to introduce Urban Air Mobility (UAM) with electrically powered, vertical take-off and landing vehicles (eVTOL). The UAM infrastructure, in particular the planning and distribution of stations (Vertiports) for eVTOL, plays a central role in the design of a sustainable air transportation system. One effective tool for simulating and analyzing future UAM station networks is the k-Means method. This algorithm groups commuter data sets into clusters. By identifying the cluster centers, potential vertiport locations can be determined and further analyzed. However, the k-Means method has some limitations such as not considering the actual geographical conditions. The research question is to what extent the k-Means method can yield relevant results for future UAM station planning. Relevant influencing factors for vertiports and their locations are examined in more detail through a corresponding literature analysis. The aim of this thesis is to develop a robust and efficient k-Means model and to analyze the resulting vertiport network. After evaluating the analysis, the connection to the vertiports and the potential time savings proved to be the most influential parameters for the station network of the k-Means models. The most promising result is the time saving model, which suggests that an optimal vertiport network consists of 70 to 80 stations. Here, 10.37 % of all ground-based trips could potentially be replaced by the eVTOL, resulting in an average travel time saving of 74.6 % and a 24.33 % reduction in the distance traveled. The biggest potential for time and distance savings is seen for journeys made by public transport and carpooling. In the future, the time savings model must be extended to include a minimum threshold for travel distance and add further detailed restricted zones. The resulting station network must then be evaluated for feasibility through a more detailed cost analysis, considering potential revenues and operational costs