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PublicationForced Motion Activated Self-Alignment of Micro-CPV Solar Cells( 2024)
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PublicationWie die Energiewende trotz des steigenden Rohstoffbedarfes gelingen kannDie Energiewende, welche aufgrund des voranschreitenden Klimawandels notwendig ist, erfordert den Einsatz neuer Technologien mit erhöhtem Rohstoffbedarf, um die ambitionierten Ziele der Klimapolitik zu erreichen. Doch wie können wir gewährleisten, dass die potenziell kritischen Rohstoffe auch in Zukunft zur Verfügung stehen?
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PublicationA forecast on future raw material demand and recycling potential of lithium-ion batteries in electric vehiclesThe market for electromobility has grown constantly in the last years. To ensure a future supply of raw materials for the production of new batteries for electric vehicles, it is essential to estimate the future demand for battery metals. This study focuses on the future demand for electric vehicle battery cathode raw materials lithium, cobalt, nickel, and manganese by considering different technology and growth scenarios. The results show that in 2040 the future material demand for lithium, cobalt, and nickel for Lithium-Ion Batteries in electric vehicles exceeds current raw material production. Depending on the growth and technology scenario, the future demand for lithium and cobalt exceeds today's production by up to 8 times in 2040. Nickel exceeds today's production in one scenario. For manganese, future demand in 2040 remains far below today's production. The recycling potential for lithium and nickel is more than half the raw material demand for Lithium-Ion Batteries in 2040. For cobalt, the recycling potential even exceeds the raw material demand in 2040. In conclusion, it remains a challenge for the industry to massively scale up resource production and focus on the recycling of battery metals in the future to meet the increasing consumption of electromobility.
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PublicationSensor Systems for Extremely Harsh Environments( 2022-10-01)Sensors are key elements for capturing environmental properties and are today indispensable in the industry for monitoring and control of industrial processes. Many applications are demanding for highly integrated intelligent sensors to meet the requirements on safety, clean, and energy-efficient operation, or to gain process information in the context of industry 4.0. While in many everyday objects highly integrated sensor systems are already state of the art, the situation in an industrial environment is clearly different. Frequently, the use of sensor systems is impossible due to the fact that the extreme ambient conditions of industrial processes like high operating temperatures or strong mechanical loads do not allow a reliable operation of sensitive electronic components. Eight Fraunhofer Institutes have bundled their competencies and have run the Fraunhofer Lighthouse Project “eHarsh” to overcome this situation. The project goal was to realize sensor systems for extremely harsh environments, whereby sensor systems are more than pure sensors, rather these are containing one or multiple sensing elements and integrated readout electronics. Various technologies, which are necessary for the realization of such sensor systems, have been identified, developed, and finally bundled in a technology platform. These technologies are, e.g., MEMS and ceramic-based sensors, SOI-CMOS-based integrated electronics, board assembly and laser-based joining technologies. All these developments have been accompanied by comprehensive tests, material characterization, and reliability simulations. Based on the platform, a pressure sensor for turbine applications has been realized to prove the performance of the eHarsh technology platform.
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PublicationFraunhofer-Institut für Zuverlässigkeit und Mikrointegration. Jahresbericht 2020/2021(Fraunhofer IZM, 2021)
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PublicationSmart sensor systems for extremely harsh environments( 2021)Sensors systems are key elements for capturing environmental properties and are increasingly important in industry 4.0 for the intelligent control of processes. However, under harsh operating conditions like high temperatures, high mechanic load or aggressive environments, standard electronics cannot be used. Eight Fraunhofer institutes have therefore bundled their competencies in sensors, microelectronics, assembly, board design, laser applications and reliability analysis to establish a technology platform for sensor systems working under extreme conditions.
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PublicationGenerative Machine Learning for Resource-Aware 5G and IoT SystemsExtrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.
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PublicationFraunhofer-Institut für Zuverlässigkeit und Mikrointegration. Jahresbericht 2019/2020(Fraunhofer IZM, 2020)
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PublicationFraunhofer-Institut für Zuverlässigkeit und Mikrointegration. Jahresbericht 2018/2019(Fraunhofer IZM, 2019)
