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The installed energy capacity of renewable energy generation systems is increasing globally due to the implementation of decarbonization policies. Due to the unpredictable nature of renewable energy sources, there is frequently a mismatch between load demand and energy supply. Stationary energy storage systems act as a buffer that stores excessive energy to balance future energy shortcomings. For residential applications, battery energy storage systems (BESS) are an attractive solution to realize the self-sufficiency of a household equipped with photovoltaics. Despite the decrease in prices, battery costs are still the most significant part of the investment cost of battery energy storage systems. Therefore, estimating battery lifetime and developing operation strategies to hinder aging is essential for improving the feasibility of BESS.
Lithium iron phosphate (LFP) lithium-ion batteries are widely used for residential BESS because of their low cost, long life, and safety. Despite extensive research in the laboratory of small-capacity cells, there are few full-scale field investigations on the lifetime and aging characteristics of commercial BESS equipped with LFP cells. This Ph.D. thesis investigates the realistic aging behavior of a residential-scale BESS equipped with large-format (180 Ah) LFP cells. We aim to create and implement a method to investigate the practical aging on cell, stack, and system levels. The experimental data is also processed by degradation modes analysis to identify the underlying mechanisms of capacity loss.
Each cell underwent primarily detailed electrical characterization, which consists of measuring characteristic charge/discharge curves and internal resistances at different current rates and temperatures. After electrical characterization, exemplary cells were opened in an inert atmosphere glove-box for structural investigation, consisting of size and weight measurements of cell components. The morphology and chemical composition of electrode samples from opened cells were investigated with light microscopy (LM) and scanning electron microscopy (SEM). The results of the detailed initial characterization of single cells were used to create a complete and self-consistent parameter set for each cell. The initial dataset was also used to compare periodical performance test results with the initial aging state of single cells throughout aging experiments.
The realistic aging experiment was carried out by investigating for 1000 days the changes in aging indicators of two commercial, residential scale BESS integrated into a microgrid. The battery stacks of both systems were built with LFP cells from the same batch of detailed characterization cells but installed with different (serial and parallel) configurations. Thus, we could investigate the effect of stack architecture on aging comparatively. At the end of the measurements, it was observed that no stack architecture is superior to another despite different operating voltages and current levels. In parallel, two LFP cells (identical to the battery stack cells) were tested at constant ambient temperature (20 °C) with continuous complete charge/discharge cycles at a constant current higher than the maximum current exhibited by BESS cells. Regarding capacity retention by equivalent full cycles, both cells outperformed BESS stacks. The comparative cell, stack, and system-level aging investigations indicate that good thermal management can provide a better lifetime even under harsher operating conditions.
The individual effects of temperature and load profile on aging were investigated via single-cell experiments in controlled ambient temperature. For this purpose, six test groups of single cells were tested to represent three realistic aging scenarios (continuous cycling, fully charged storage, and partially charged storage) at two different ambient temperatures (35 °C and 50 °C). All cells tested at 50 °C aged faster than those tested at 35 °C according to periodical performance diagnostics. Continuous cycling increased capacity loss among the cells tested at the same ambient temperature compared to fully or partially charged storage.
Single-cell experiment data was analyzed using degradation mode analysis algorithms. The results demonstrate that the loss of lithium inventory, attributed to the irreversible loss of lithium due to continuous growth of the solid electrolyte interface (SEI) layer, is the primary aging mode in all cases.
Seit ihrer Markteinführung Anfang der neunziger Jahre spielen Lithium-Ionen-Batterie (LIB) eine wichtige Rolle in der Gesellschaft und Entwicklung neuer Technologien. Mit zunehmendem Bewusstsein für den Klimawandel wurde auch die Notwendigkeit erkannt, fossile Energieträger durch Alternativen zu ersetzen. Hierbei spielen LIB in einer Vielzahl von Anwendungen eine entscheidende Rolle. Zu finden sind sie sowohl im stationären Bereich wie z. B. Heimspeicher, als auch mobilen Anwendungen von der Armbanduhr bis hin zu Fähren. Neue Einsatzgebiete bedeuten dabei immer größer werdende Herausforderungen, die LIBn auf die jeweiligen Anforderungen abzustimmen. Mathematische Modelle spielen in der Forschung und Entwicklung von LIB einen elementaren Bestandteil. Mithilfe von numerischer Simulation wird das Verhalten der LIB nachgebildet, um ein besseres Verständnis für deren Verhalten zu bekommen.
Diese Arbeit beschäftigt sich mit der Entwicklung, Parametrierung und experimentellen Validierung eines pseudo-3D (P3D) multiphysikalischen Modells einer kommerziellen 350 mAh Hochleistungs- (HP) Lithium-Ionen-Pouchzelle. Die LIB hat eine Graphitanode und eine Lithium-Kobaltoxid (LCO)/Lithium-Nickel-Kobalt-Aluminiumoxid (NCA) Mischkathode. Das Modell beschreibt die Transportprozesse auf den drei folgenden Skalen: Wärmetransport auf der Zellebene (Makroskala) – Massen- und Ladungstransfer auf der Elektrodenpaarebene (Mesoskala) – Massentransport im Aktivmaterial (Mikroskala). Mithilfe der Open-Source-Software Cantera zur Berechnung der chemischen Kinetik wird eine verallgemeinerte Beschreibung der elektrochemischen Vorgänge in der Mischelektrode entwickelt. Zum Ende der Modellerstellung kann eine sehr gute Übereinstimmung der Simulationsergebnisse mit galvanostatischen Lade-/Entlademessungen, elektrochemischen Impedanzspektroskopie (EIS) und Oberflächentemperaturmessungen über einen weiten Bereich von Betriebsbedingungen (0,05 C bis 10 C Ladung und Entladung, 5 °C bis 35 °C) gezeigt werden. Anschließend werden Methoden entwickelt, die Verluste, die in einer LIB während dem Betrieb entstehen, auf deren Ursachen zurückführen zu können. Die Verluste äußern sich in Überspannungen während der Lade und Entladevorgänge. Hierbei finden drei verschiedene Methoden ihre Anwendungen: (1) Aufteilung der Überspannungsbeiträge entsprechend ihrer Ursachen hinsichtlich der ohmschen Anteile sowie der Konzentrations- und Aktivierungsanteile der einzelnen Zellkomponenten. (2) Die partielle elektrochemische Impedanzspektroskopie zur Darstellung der verschiedenen Beiträge der EIS. (3) Sensitivitätsanalyse hinsichtlich globaler Kennwerte (Kapazität, Innenwiderstand und Impedanz) und der Überspannungen zur Bewertung des Einflusses der verschiedenen Zellparameter hinsichtlich der Simulationsergebnisse. Zum Abschluss wird der Zielkonflikt zwischen Energie und Leistung betrachtet, der insbesondere auch bei Elektrofahrzeugen eine Rolle spielt, da große Reichweiten (viel Energie) und möglichst kurze Ladezeiten (hohe Leistung) gefordert werden. Es wird gezeigt, dass Lithiumplating durch hohe Temperaturen oder durch die Verwendung des CCCPCV-Ladeprotokolls, mit einer Phase mit konstantem Anodenpotential, vermieden werden kann. Es wird eine spezifische Ladeleistung eingeführt und diese als das Verhältnis zwischen der entladenen Energie (bei langsamer Entladung) und der erforderlichen Ladezeit (bei schneller Ladung) quantifiziert. Die Wert weisen ein deutliches Optimum in Bezug auf die Elektrodendicke auf. Bei 35 °C wurde das Optimum mit einem Hochenergie-Elektrodendesign erreicht, das 23,8 Wh/(min·l) spezifische Ladeleistung bei 15,2 min Ladezeit (10 % bis 80 % SOC) und 517 Wh/l Entladeenergiedichte ergab. Durch die Analyse der verschiedenen Überspannungsbeiträge konnten wir zeigen, dass Elektrolyt-Transportverluste für die unzureichende Lade- und Entladeleistung von Zellen mit sehr dicken Elektroden hauptverantwortlich sind.
The last decades have seen the evolution of industrial production into more sophisticated processes. The development of specialized, high-end machines has increased the importance of predictive maintenance of mechanical systems to produce high-quality goods and avoid machine breakdowns. Predictive maintenance has two main objectives: to classify the current status of a machine component and to predict the maintenance interval by estimating its remaining useful life (RUL). Nowadays, both objectives are covered by machine learning and deep learning approaches and require large training datasets that are often not available. One possible solution may be transfer learning, where the knowledge of a larger dataset is transferred to a smaller one. This thesis is primarily concerned with transfer learning for predictive maintenance for fault classification and RUL estimation. The first part presents the state-of-the-art machine learning techniques with a focus on techniques applicable to predictive maintenance tasks (Chapter 2). This is followed by a presentation of the machine tool background and current research that applies the previously explained machine learning techniques to predictive maintenance tasks (Chapter 3). One novelty of this thesis is that it introduces a new intermediate domain that represents data by focusing on the relevant information to allow the data to be used on different domains without losing relevant information (Chapter 4). The proposed solution is optimized for rotating elements. Therefore, the presented intermediate domain creates different layers by focusing on the fault frequencies of the rotating elements. Another novelty of this thesis is its semi and unsupervised transfer learning-based fault classification approach for different component types under different process conditions (Chapter 5). It is based on the intermediate domain utilized by a convolutional neural network (CNN). In addition, a novel unsupervised transfer learning loss function is presented based on the maximum mean discrepancy (MMD), one of the state-of-the-art algorithms. It extends the MMD by considering the intermediate domain layers; therefore, it is called layered maximum mean discrepancy (LMMD). Another novelty is an RUL estimation transfer learning approach for different component types based on the data of accelerometers with low sampling rates (Chapter 6). It applies the feature extraction concepts of the classification approach: the presented intermediate domain and the convolutional layers. The features are then used as input for a long short-term memory (LSTM) network. The transfer learning is based on fixed feature extraction, where the trained convolutional layers are taken over. Only the LSTM network has to be trained again. The intermediate domain supports this transfer learning type, as it should be similar for different component types. In addition, it enables the practical usage of accelerometers with low sampling rates during transfer learning, which is an absolute novelty. All presented novelties are validated in detailed case studies using the example of bearings (Chapter 7). In doing so, their superiority over state-of-the-art approaches is demonstrated.
With the expansion of IoT devices in many aspects of our life, the security of such systems has become an important challenge. Unlike conventional computer systems, any IoT security solution should consider the constraints of these systems such as computational capability, memory, connectivity, and power consumption limitations. Physical Unclonable Functions (PUFs) with their special characteristics were introduced to satisfy the security needs while respecting the mentioned constraints. They exploit the uncontrollable and reproducible variations of the underlying component for security applications such as identification, authentication, and communication security. Since IoT devices are typically low cost, it is important to reuse existing elements in their hardware (for instance sensors, ADCs, etc.) instead of adding extra costs for the PUF hardware. Micro-electromechanical system (MEMS) devices are widely used in IoT systems as sensors and actuators. In this thesis, a comprehensive study of the potential application of MEMS devices as PUF primitives is provided. MEMS PUF leverages the uncontrollable variations in the parameters of MEMS elements to derive secure keys for cryptographic applications. Experimental and simulation results show that our proposed MEMS PUFs are capable of generating enough entropy for a complex key generation, while their responses show low fluctuations in different environmental conditions.
Keeping in mind that the PUF responses are prone to change in the presence of noise and environmental variations, it is critical to derive reliable keys from the PUF and to use the maximum entropy at the same time. In the second part of this thesis, we elaborate on different key generation schemes and their advantages and drawbacks. We propose the PUF output positioning (POP) and integer linear programming (ILP) methods, which are novel methods for grouping the PUF outputs in order to maximize the extracted entropy. To implement these methods, the key enrollment and key generation algorithms are presented. The proposed methods are then evaluated by applying on the responses of the MEMS PUF, where it can be practically shown that the proposed method outperforms other existing PUF key generation methods.
The final part of this thesis is dedicated to the application of the MEMS PUF as a security solution for IoT systems. We select the mutual authentication of IoT devices and their backend system, and propose two lightweight authentication protocols based on MEMS PUFs. The presented protocols undergo a comprehensive security analysis to show their eligibility to be used in IoT systems. As the result, the output of this thesis is a lightweight security solution based on MEMS PUFs, which introduces a very low overhead on the cost of the hardware.
Ultra-low-power passive telemetry systems for industrial and biomedical applications have gained much popularity lately. The reduction of the power consumption and size of the circuits poses critical challenges in ultra-low-power circuit design. Biotelemetry applications like leakage detection in silicone breast implants require low-power-consuming small-size electronics. In this doctoral thesis, the design, simulation, and measurement of a programmable mixed-signal System-on-Chip (SoC) called General Application Passive Sensor Integrated Circuit (GAPSIC) is presented. Owing to the low power consumption, GAPSIC is capable of completely passive operation. Such a batteryless passive system has lower maintenance complexity and is also free from battery-related health hazards. With a die area of 4.92 mm² and a maximum analog power consumption of 592 µW, GAPSIC has one of the best figure-of-merits compared to similar state-of-the-art SoCs. Regarding possible applications, GAPSIC can read out and digitally transmit the signals of resistive sensors for pressure or temperature measurements. Additionally, GAPSIC can measure electrocardiogram (ECG) signals and conductivity.
The design of GAPSIC complies with the International Organization for Standardization (ISO) 15693/NFC (near field communication) 5 standard for radio frequency identification (RFID), corresponding to the frequency range of 13.56 MHz. A passive transponder developed with GAPSIC comprises of an external memory storage and very few other external components, like an antenna and sensors. The passive tag antenna and reader antenna use inductive coupling for communication and energy transfer, which enables passive operation. A passive tag developed with GAPSIC can communicate with an NFC compatible smart device or an ISO 15693 RFID reader. An external memory storage contains the programmable application-specific firmware.
As a mixed-signal SoC, GAPSIC includes both analog and digital circuitries. The analog block of GAPSIC includes a power management unit, an RFID/NFC communication unit, and a sensor readout unit. The digital block includes an integrated 32-bit microcontroller, developed by the Hochschule Offenburg ASIC design center, and digital peripherals. A 16-kilobyte random-access memory and a read-only 16-kilobyte memory constitute the GAPSIC internal memory. For the fabrication of GAPSIC, one poly, six-metal 0.18 µm CMOS process is used.
The design of GAPSIC includes two stages. In the first stage, a standalone RFID/NFC frontend chip with a power management unit, an RFID/NFC communication unit, a clock regenerator unit, and a field detector unit was designed. In the second stage, the rest of the functional blocks were integrated with the blocks of the RFID/NFC frontend chip for the final integration of GAPSIC. To reduce the power consumption, conventional low-power design techniques were applied extensively like multiple power supplies, and the operation of complementary metal-oxide-semiconductor (CMOS) transistors in the sub-threshold region of operation, as well as further innovative circuit designs.
An overvoltage protection circuit, a power rectifier, a bandgap reference circuit, and two low-dropout (LDO) voltage regulators constitute the power management unit of GAPSIC. The overvoltage protection circuit uses a novel method where three stacked transistor pairs shunt the extra voltage. In the power rectifier, four rectifier units are arranged in parallel, which is a unique approach. The four parallel rectifier units provide the optimal choice in terms of voltage drop and the area required.
The communication unit is responsible for RFID/NFC communication and incorporates demodulation and load modulation circuitry. The demodulator circuit comprises of an envelope detector, a high-pass filter, and a comparator. Following a new approach, the bandgap reference circuit itself acts as the load for the envelope detector circuit, which minimizes the circuit complexity and area. For the communication between the reader and the RFID/NFC tag, amplitude-shift keying (ASK) is used to modulate signals, where the smallest modulation index can be as low as 10%. A novel technique involving a comparator with a preset offset voltage effectively demodulates the ASK signal. With an effective die area of 0.7 mm² and power consumption of 107 µW, the standalone RFID/NFC frontend chip has the best figure-of-merits compared to the state-of-the-art frontend chips reported in the relevant literature. A passive RFID/NFC tag developed with the standalone frontend chip, as well as temperature and pressure sensors demonstrate the full passive operational capability of the frontend chip. An NFC reader device using a custom-built Android-based application software reads out the sensor data from the passive tag.
The sensor readout circuit consists of a channel selector with two differential and four single-ended inputs with a programmable-gain instrumentation amplifier. The entire sensor readout part remains deactivated when not in use. The internal memory stores the measured offset voltage of the instrumentation amplifier, where a firmware code removes the offset voltage from the measured sensor signal. A 12-bit successive approximation register (SAR) type analog-to-digital-converter (ADC) based on a charge redistribution architecture converts the measured sensor data to a digital value. The digital peripherals include a serial peripheral interface, four timers, RFID/NFC interfaces, sensor readout unit interfaces, and 12-bit SAR logic.
Two sets of studies with custom-made NFC tag antennas for biomedical applications were conducted to ascertain their compatibility with GAPSIC. The first study involved the link efficiency measurements of NFC tag antennas and an NFC reader antenna with porcine tissue. In a separate experiment, the effect of a ferrite compared to air core on the antenna-coupling factor was investigated. With the ferrite core, the coupling factor increased by four times.
Among the state-of-the-art SoCs published in recent scientific articles, GAPSIC is the only passive programmable SoC with a power management unit, an RFID/NFC communication interface, a sensor readout circuit, a 12-bit SAR ADC, and an integrated 32-bit microcontroller. This doctoral research includes the preliminary study of three passive RFID tags designed with discrete components for biomedical and industrial applications like measurements of temperature, pH, conductivity, and oxygen concentration, along with leakage detection in silicone breast implants. Besides its small size and low power consumption, GAPSIC is suitable for each of the biomedical and industrial applications mentioned above due to the integrated high-performance microcontroller, the robust programmable instrumentation amplifier, and the 12-bit analog-to-digital converter. Furthermore, the simulation and measurement data show that GAPSIC is well suited for the design of a passive tag to monitor arterial blood pressure in patients experiencing Peripheral Artery Disease (PAD), which is proposed in this doctoral thesis as an exemplary application of the developed system.
Virtual-Reality
(2023)
Die Virtual-Reality (VR) Technologie ermöglicht Unternehmen eine Produktpräsentation, die weit über traditionelle Darstellungsmethoden hinausgeht. Obgleich die Integration der VR-Technologie für Unternehmen viele Chancen eröffnet, ist deren Einsatz auch mit Risiken verbunden. Insbesondere der Mangel an empirisch gesicherten Erkenntnissen zur Kundenakzeptanz, zu den Auswirkungen der Nutzung sowie zu Kannibalisierungseffekten ist ein wesentlicher Grund, der die Verbreitung von VR in der Kundenkommunikation noch hemmt. Das Buch adressiert diese Forschungslücken und identifiziert mittels eines nutzerzentrierten, quantitativen Forschungsdesigns konkrete Chancen und Risiken, die mit dem Einsatz von VR-Produktpräsentationen verbunden sind.
Due to its performance, the field of deep learning has gained a lot of attention, with neural networks succeeding in areas like Computer Vision (CV), Neural Language Processing (NLP), and Reinforcement Learning (RL). However, high accuracy comes at a computational cost as larger networks require longer training time and no longer fit onto a single GPU. To reduce training costs, researchers are looking into the dynamics of different optimizers, in order to find ways to make training more efficient. Resource requirements can be limited by reducing model size during training or designing more efficient models that improve accuracy without increasing network size.
This thesis combines eigenvalue computation and high-dimensional loss surface visualization to study different optimizers and deep neural network models. Eigenvectors of different eigenvalues are computed, and the loss landscape and optimizer trajectory are projected onto the plane spanned by those eigenvectors. A new parallelization method for the stochastic Lanczos method is introduced, resulting in faster computation and thus enabling high-resolution videos of the trajectory and secondorder information during neural network training. Additionally, the thesis presents the loss landscape between two minima along with the eigenvalue density spectrum at intermediate points for the first time.
Secondly, this thesis presents a regularization method for Generative Adversarial Networks (GANs) that uses second-order information. The gradient during training is modified by subtracting the eigenvector direction of the biggest eigenvalue, preventing the network from falling into the steepest minima and avoiding mode collapse. The thesis also shows the full eigenvalue density spectra of GANs during training.
Thirdly, this thesis introduces ProxSGD, a proximal algorithm for neural network training that guarantees convergence to a stationary point and unifies multiple popular optimizers. Proximal gradients are used to find a closed-form solution to the problem of training neural networks with smooth and non-smooth regularizations, resulting in better sparsity and more efficient optimization. Experiments show that ProxSGD can find sparser networks while reaching the same accuracy as popular optimizers.
Lastly, this thesis unifies sparsity and neural architecture search (NAS) through the framework of group sparsity. Group sparsity is achieved through ℓ2,1-regularization during training, allowing for filter and operation pruning to reduce model size with minimal sacrifice in accuracy. By grouping multiple operations together, group sparsity can be used for NAS as well. This approach is shown to be more robust while still achieving competitive accuracies compared to state-of-the-art methods
This thesis deals with the redesign of manufacturing systems by simulation and optimization. Material flow simulation is a common tool for solving problems in system design. Limitations are the high requirements in time and knowledge to execute simulation studies, evaluate results and solve design problems. New chances arrives with the technologies of industry 4.0 and the digital shadow, providing data for simulation. However, the methods to use production data for the redesign of production systems are not available yet. Purpose of this work is providing the methods to automate simulation from digital shadow, use simulation to optimize and solve problems in system design. Two case studies are used to support the action research approach of this work. The result of this work is a framework for the application of the digital shadow in optimization and problem-solving.
A report from the World Economic Forum (2019) stated loneliness as the third societal stressor in the world, mainly in western countries. Moreover, research shows that loneliness tends to be experienced more severely by young adults than other age groups (Rokach, 2000), which is the case of university students who face profound periods of loneliness when attending university in a new place (Diehl et al., 2018). Digital technology, especially mental health apps (MHapps), have been viewed as promising solutions to address this distress in universities, however, little evidence on this topic reveals uncertainty around how these resources impact individual well-being. Therefore, this research proposed to investigate how the gamified social mobile app Noneliness reduced loneliness rates and other associated mental health issues of students from a German university. As little work has focused on digital apps targeting loneliness, this project also proposed to describe and discuss the app’s design and development processes. A multimethod approach was adopted: literature review on high-efficacy MHapps design, gamification for mental health and loneliness interventions; User Experience Design and Human-centered Computing. Evaluations occurred according to the app’s development iterations, which assessed four versions (from prototype to Beta) through quantitative and qualitative studies with university students. The main results obtained regarding the design aspects were: users' preference for minimalistic interfaces; importance in maintaining privacy and establishing trust among users; students' willingness to use an online support space for emotional and educational support. Most used features were those related to group discussions, private chats and university social events. Preferred gamification elements were those that provided positive reinforcement to motivate social interactions (e.g. Points, Levels and Achievements). Results of a pilot randomized controlled trial with university students (N = 12), showed no statistically significant interactions in reducing loneliness among experimental group members (n = 7, x² = 3.500, p-value = 0.477, Cramer’s V = 0.27) who made continued use of the app for six weeks. On the other hand, the app showed effects of moderate magnitude on loneliness reduction in this group. The app also demonstrated relatively strong magnitude effects on other associated variables, such as depression and stress in the experimental group. In addition to motivating the conduct of further studies with larger samples, the findings point to a potential app effectiveness not only to reduce loneliness, but also other variables that may be associated with the distress.
Electrochemical pressure impedance spectroscopy (EPIS) has received the attention of researchers as a method to study mass transport processes in polymer electrolyte mem-brane fuel cells (PEMFC). It is based on analyzing the cell voltage response to a harmonic excitation of the gas phase pressure in the frequency domain. Several experiments with a single-cell fuel cell have shown that the spectra contain information in the frequency range typical for mass transport processes and are sensitive to specific operating condi-tions and structural fuel cell parameters. To further benefit from the observed features, it is essential to identify why they occur, which to date has not yet been accomplished. The aim of the present work, therefore, is to identify causal links between internal processes and the corresponding EPIS features.
To this end, the study follows a model-based approach, which allows the analysis of inter-nal states that are not experimentally accessible. The PEMFC model is a pseudo-2D model, which connects the mass transport along the gas channel with the mass transport through the membrane electrode assembly. A modeling novelty is the consideration of the gas vol-ume inside the humidifier upstream the fuel cell inlet, which proves to be crucial for the reproduction of EPIS. The PEMFC model is parametrized to a 100 cm² single cell of the French project partner, who provided the experimental EPIS results reproduced and in-terpreted in the present study.
The simulated EPIS results show a good agreement with the experiments at current den-sities ≤ 0.4 A cm–2, where they allow a further analysis of the observed features. At the lowest excitation frequency of 1 mHz, the dynamic cell voltage response approaches the static pressure-voltage response. In the simulated frequency range between 1 mHz – 100 Hz, the cell voltage oscillation is found to strongly correlate with the partial pressure oscillation of oxygen, whereas the influence of the water pressure is limited to the low frequency region.
The two prominent EPIS features, namely the strong increase of the cell voltage oscillation and the increase of phase shift with frequency, can be traced back via the oxygen pressure to the oscillation of the inlet flow rate. The phenomenon of the oscillating inlet flow rate is a consequence of the pressure change of the gas phase inside the humidifier and in-creases with frequency. This important finding enables the interpretation of experimen-tally observed EPIS trends for a variation of operational and structural fuel cell parame-ters by tracing them back to the influence of the oscillating inlet flow rate.
The separate simulation of the time-dependent processes of the PEMFC model through model reduction shows their individual influence on EPIS. The sluggish process of the wa-ter uptake by the membrane is visible below 0.1 Hz, while the charge and discharge of the double layer becomes visible above 1 Hz. The gas transport through the gas diffusion layer is only visible above 100 Hz. The simulation of the gas transport through the gas channel
without consideration of the humidifier becomes visible above 1 Hz. With consideration of the humidifier the gas transport through the gas channel is visible throughout the fre-quency range. The strong similarity of the spectra considering the humidifier with the spectra of the full model setup shows the dominant influence of the humidifier on EPIS.
A promising observation is the change in the amplitude relationship between the cell volt-age and the oxygen partial pressure oscillation as a function of the oxygen concentration in the catalyst layer. At a frequency where the influence of oxygen pressure on the cell voltage is dominant, for example at 1 Hz, the amplitude of the cell voltage oscillation could be used to indirectly measure the oxygen concentration in the catalyst layer.