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The European Society for New Methods in Agricultural Research (ESNA) is an international society originally established in Wageningen (The Netherlands) in 1969 with the aims of exchanging ideas and techniques to promote the advancement of agricultural sciences. The original scope - the co-ordination of research in the application of nuclear techniques in agriculture - has gradually changed and now the Society also covers aspects of environmental protection and the application of new methods and biotechnology in agricultural research. The Society organizes annual meetings in various European countries and the scientific programme is devoted to fundamental and applied issues from the above-mentioned areas. For more information see http://www.mendelu.cz/esna/. One of the 6 working groups, where current research is presented as oral papers or posters is Working Group 3 with the scientific topic SOIL PLANT RELATIONSHIPS, comprising soil science, plant nutrition (including microbial aspects), application of stable and radioactive isotope techniques, plant physiology, behaviour of pollutants in soil-plant system. Proceedings of Working Group 3 from the annual meetings since 2001 are published here. Present Chairmen of this working group are V. Licina and. G. Zibold. Professor Dr. V. Licina, University of Belgrade, Faculty of Agriculture, 11 080 Belgrade, Nemanjina 6, Serbia&Montenegro; E-mail: licina@agrifaculty.bg.ac.yu; Professor Dr. G. Zibold, University of Applied Sciences Fachhochschule Ravensburg-Weingarten, D 88241 Weingarten, Germany; E-mail: zibold@fh-weingarten.de
Robotic grasping has been a prevailing problem ever since
humans began creating robots to execute human-like tasks. The problems
are usually due to the involvement of moving parts and sensors. Inaccuracy in sensor data usually leads to unexpected results. Researchers have
used a variety of sensors for improving manipulation tasks in robots.
We focus specifically on grasping unknown objects using mobile service
robots. An approach using convolutional neural networks to generate
grasp points in a scene using RGBD sensor data is proposed. Two convolutional neural networks that perform grasp detection in a top down
scenario are evaluated, enhanced and compared in a more general scenario. Experiments are performed in a simulated environment as well as
the real world. The results are used to understand how the difference in
sensor data can affect grasping and enhancements are made to overcome
these effects and to optimize the solution.
Bicycle-drawn cargo trailers with an electric drive to enable the transportation of high cargo loads are used as part of the last-mile logistics. Depending on the load, the total mass of a trailer can vary between approx. 50 and 250 kg, potentially more than the mass of the towing bicycle. This can result in major changes in acceleration and braking behavior of the overall system. While existing systems are designed primarily to provide sufficient power, improvements are needed in the powertrain control system in terms of driver safety and comfort. Hence, we propose a novel prototype that allows measurement of the tensile force in the drawbar which can subsequently be used to design a superior control system. In this context, a sinusoidal force input from the cyclist to the trailer according to the cadence of the cyclist is observed. The novelty of this research is to analyze whether torque impulses of the cyclist can be reduced with the help of Model Predictive Control (MPC). In addition, the powertrain of the trailer is intended to support the braking process of the system with regenerative braking. In the context of this research, a first MPC controller design is carried out and analyzed with the help of a Hardware-in-the-Loop (HIL) approach where the microcontroller of the power electronics is included as hardware to ensure the vehicle dynamics control interacts properly with the lower-level field-oriented control. The battery and motor subsystems are simulated in a Typhoon HIL 604, which is supplemented by a vehicle dynamics model of the trailer that is integrated as a Functional Mock-Up Unit (FMU). First results indicate that the MPC longitudinal dynamics controller supports the driver during acceleration, attenuates the sinusoidal oscillations and reduces the force with which the trailer pushes the bicycle during braking.
Battery electric vehicle (BEV) adoption and complex powertrains
pose new challenges to automotive industries, requiring
comprehensive testing and validation strategies for reliability and
safety. Hardware-in-the-loop (HIL) based real-time simulation is
important, with cooperative simulation (co-simulation) being an
effective way to verify system functionality across domains. Fault
injection testing (FIT) is crucial for standards like ISO 26262.
This study proposes a HIL-based real-time co-simulation
environment that enables fault injection tests in BEVs to allow
evaluation of their effects on the safety of the vehicle. A Typhoon
HIL system is used in combination with the IPG CarMaker
environment. A four-wheel drive BEV model is built, considering
high-fidelity electrical models of the powertrain components
(inverter, electric machine, traction battery) and the battery
management system (BMS). Additionally, it enables validation of
driving dynamics, routes and environmental influences and provides
a precise analysis of the effect of powertrain system faults on driving
behavior. A possible case for a fault injection is to introduce a shootthrough fault in the inverter. Through the co-simulation, it is possible
to analyze the effects on the powertrain and the vehicle dynamics in
different driving situations (e.g. snow). This work demonstrates that
co-simulation is a valuable tool for the development and validation of
BEVs, and presents specific fault cases introduced into the
powertrain and the resulting effects tested under different driving
conditions. In addition, the study discusses the system's limitations
and future possibilities such as controller hardware integration
(Controller-HIL) and autonomous driving system validation.
The power density of electric machines is a critical factor in various applications, i.e. like the power train. A major factor to improve the power density is boosting the electric current density, which increases the losses in the limited volume of the electric machine. This results in a need for an optimized thermal design and efficient cooling. The dissipation of heat can be achieved in a multitude of ways, ranging from air cooling to highly integrated cooling solutions. In this paper, this variety is shown and analyzed with a focus on water cooling. Further various structures in electric machines are presented.
A planar testbench is built to systematically analyze water cooling geometries. The focus lies in providing different power loss distributions along cooling channels, accurate temperature readings in a multitude of locations, as well as the pressure drop across the channel. The test bench results are aligned with simulations and simplified analytical evaluation to support the development process.
The main goal in this paper is to determine temperature gradients in the material close to the stator to quantize the potential for future cooling jacket designs. One question ,to answer is: How large the gradient is considering a realistic power loss distribution. Another sensible point are the different thermal expansions of aluminum used in cooling jackets and the steel core of the stator. This can be bypassed by using a steel cooling jacket. In this case, the performance of a steel cooling jacket compared to an aluminum version is investigated and also if light weight construction can compensate the lower thermal conductivity of steel.
After the analysis, an outlook about future changes of the measurement methods are given and first potentials for future cooling jackets are proposed.