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Institute
- Fachbereich Luft- und Raumfahrttechnik (776) (remove)
Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
This dataset was acquired at field tests of the steerable ice-melting probe "EnEx-IceMole" (Dachwald et al., 2014). A field test in summer 2014 was used to test the melting probe's system, before the probe was shipped to Antarctica, where, in international cooperation with the MIDGE project, the objective of a sampling mission in the southern hemisphere summer 2014/2015 was to return a clean englacial sample from the subglacial brine reservoir supplying the Blood Falls at Taylor Glacier (Badgeley et al., 2017, German et al., 2021).
The standardized log-files generated by the IceMole during melting operation include more than 100 operational parameters, housekeeping information, and error states, which are reported to the base station in intervals of 4 s. Occasional packet loss in data transmission resulted in a sparse number of increased sampling intervals, which where compensated for by linear interpolation during post processing. The presented dataset is based on a subset of this data: The penetration distance is calculated based on the ice screw drive encoder signal, providing the rate of rotation, and the screw's thread pitch. The melting speed is calculated from the same data, assuming the rate of rotation to be constant over one sampling interval. The contact force is calculated from the longitudinal screw force, which es measured by strain gauges. The used heating power is calculated from binary states of all heating elements, which can only be either switched on or off. Temperatures are measured at each heating element and averaged for three zones (melting head, side-wall heaters and back-plate heaters).
Impact of electric propulsion technology and mission requirements on the performance of VTOL UAVs
(2018)
One of the engineering challenges in aviation is the design of transitioning vertical take-off and landing (VTOL) aircraft. Thrust-borne flight implies a higher mass fraction of the propulsion system, as well as much increased energy consumption in the take-off and landing phases. This mass increase is typically higher for aircraft with a separate lift propulsion system than for aircraft that use the cruise propulsion system to support a dedicated lift system. However, for a cost–benefit trade study, it is necessary to quantify the impact the VTOL requirement and propulsion configuration has on aircraft mass and size. For this reason, sizing studies are conducted. This paper explores the impact of considering a supplemental electric propulsion system for achieving hovering flight. Key variables in this study, apart from the lift system configuration, are the rotor disk loading and hover flight time, as well as the electrical systems technology level for both batteries and motors. Payload and endurance are typically used as the measures of merit for unmanned aircraft that carry electro-optical sensors, and therefore the analysis focuses on these particular parameters.