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275 results on '"Ebel BE"'

1. A Communication-Free and Model-Free Predictive Control for a Dynamic IPT System With High Power Factor for Electric Vehicles

Author

Sina Navaiyan Kalat, Sadegh Vaez-Zadeh, Ali Zakerian, Amir Babaki, and Thomas Ebel

Subjects
Constant voltage, dynamic inductive power transfer, electric vehicles, frequency optimization, group-based control, model-free predictive control, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Facilitating charging of Electric Vehicles (EVs), dynamic Inductive Power Transfer (IPT) technology has recently gained considerable attention. Yet, stabilizing the transferred power under different load resistances and coupling coefficients is still an issue. On the other hand, realizing high power factor (PF) operation in constant voltage (CV) operation increases the life of equipment in the system. A new model-free predictive control (MFPC) for DWPT systems is proposed in this paper based on the frequency optimization. The imaginary part of the input impedance is expressed as a function mega parameter. The control system becomes independent of the system parameter by calculating the mega parameters. Also, the group-based control approach used in the MFPC method, reduces the computing burden, improves the system dynamics, and avoids unsafe operating points. Moreover, the system output voltage is regulated by adjusting the duty cycle of the inverter. Simulation and experimental results validate the effectiveness of the proposed method.

Published
2023
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2. Electric Vehicle Load Management: An Architecture for Heterogeneous Nodes

Author

Kun Qian, Robert Brehm, Thomas Ebel, and Rebecca C. Adam

Subjects
Electric vehicles, scheduling strategy, aggregator, control architecture, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Integrating electric vehicle (EV) charging infrastructures into the utility grids requires solutions for coordinated charging of EVs. Load management systems aim at computing coordinated charging schedules for electric vehicles based on predetermined charging objectives. Contributions on coordinated charging for EVs predominantly assume that the EVs or the charging stations are controllable entities in the load management systems. However, in practice, charging infrastructures may consist of controllable and uncontrollable entities. This paper proposes architecture and control strategies for EV charging infrastructures consisting of controllable and uncontrollable entities. Simulations based on real-world charging sessions show how the share of uncontrollable entities in a charging infrastructure affects the performance of different control strategies in the system architecture. We show that a certain number of uncontrollable entities in a charging infrastructure does not affect the scheduling objectives significantly. EV fleet and charging infrastructure operators can develop pragmatic investment and operation strategies based on the proposed control strategy and architecture.

Published
2022
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3. Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Author

Sudipan Saha, Muhammad Shahzad, Patrick Ebel, and Xiao Xiang Zhu

Subjects
Change detection (CD), fusion, multisensor analysis, optical images, Siamese network, synthetic aperture radar (SAR), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Change detection using satellite/aerial images is used to quantify the impacts of many natural and man-made disasters. At the occurrence of such events, both prechange optical and synthetic aperture radar (SAR) images can be obtained by going back in time. However, the availability of the postchange optical image is often hindered by the presence of artifacts like clouds. To circumnavigate this, we propose a novel change detection data setting that uses both optical and SAR images prechange, yet only SAR imagery postchange. For this challenging scenario, we propose a Siamese network that processes the prechange and postchange SAR inputs using a shared set of weights, while the prechange optical input is processed using a network that do not share the weights with the SAR inputs. The encoded weights from the three networks are fused and finally decoded using a common decoder to obtain the change map. Our model effectively fuses multisensor information and can obtain satisfactory result despite the absence of the postchange optical image. Experimental results on a multisensor urban dataset demonstrate the effectiveness of the proposed approach.

Published
2022
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4. Explaining the Effects of Clouds on Remote Sensing Scene Classification

Author

Jakob Gawlikowski, Patrick Ebel, Michael Schmitt, and Xiao Xiang Zhu

Subjects
Classification, clouds, deep learning, explainability, remote sensing, robustness, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Most of Earth is covered by haze or clouds, impeding the constant monitoring of our planet. Preceding works have documented the detrimental effects of cloud coverage on remote sensing applications and proposed ways to approach this issue. However, up to now, little effort has been spent on understanding how exactly atmospheric disturbances impede the application of modern machine learning methods to Earth observation data. Specifically, we consider the effects of haze and cloud coverage on a scene classification task. We provide a thorough investigation of how classifiers trained on cloud-free data fail once they encounter noisy imagery—a common scenario encountered when deploying pretrained models for remote sensing to real use cases. We show how and why remote sensing scene classification suffers from cloud coverage. Based on a multistage analysis, including explainability approaches applied to the predictions, we work out four different types of effects that clouds have on scene prediction. The contribution of our work is to deepen the understanding of the effects of clouds on common remote sensing applications and consequently guide the development of more robust methods.

Published
2022
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23. Electrophysiological Biomarkers of Epileptic Tissue in Human Brain Epilepsy

Author

Lepkova, Kamila, primary, Nejedly, Petr, additional, Sladky, Vladimir, additional, Mivalt, Filip, additional, Krsek, Pavel, additional, Kudr, Martin, additional, Ebel, Matyas, additional, Marusic, Petr, additional, Krysl, David, additional, Kalina, Adam, additional, Janca, Radek, additional, Kremen, Vaclav, additional, and Worrell, Gregory A, additional

Published
2022
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36. Lyapunov Function based Stability Analysis of Symmetric Induction Machine

Author

Thomas Ebel, Ramkrishan Maheshwari, Deepak U. Patil, and Sanjeet Dwivedi

Subjects
Lyapunov function, Electric machine, Operating point, Region-of-attraction, business.product_category, Computer science, Stability (learning theory), symbols.namesake, Nonlinear system, Induction machine, Control theory, symbols, business
Abstract

The induction machine is considered as the most reliable and robust electric machine and widely used for various applications. The induction machine is a nonlinear system. Therefore, its operating point stability analysis can be done using the Lyapunov function approach. In this paper, a model of the induction machine is analyzed, and an approach to find a Lyapunov function for the machine is discussed. The Lyapunov function has its region-of-attraction which is also analyzed in the paper. The simulation results are shown to support analysis.

Published
2021
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37. Optimal gapped boost in inductor design for power factor correction applications

Author

Wai Keung Mo, Kasper Paasch, and Thomas Ebel

Subjects
Active filter, Harmonics, Magnetic device, Winding topology, Physics::Classical Physics, Boost, Simulation, Computer Science::Other
Abstract

This paper summarizes the winding geometrical and core gap effects on inductor winding loss. Relevant DC and AC winding resistance equations are derived, and an optimal inductor design is given for the ratio of direct to ripple current ${\text{k}} = \frac{{{I_{av}}}}{{{{\Delta }}I}}$. A new concept of a curve-gapped inductor solution is proposed to improve the line harmonic current for lighting equipment.

Published
2021

38. Active Damping by State Feedback Control in Induction Motor Drive with Filter

Author

Prasun Mishra, Stig Munk-Neilsen, Ramkrishan Maheshwari, and Thomas Ebel

Subjects
Active damping, Field Oriented Control, Induction motor, Passive Filter
Abstract

The use of passive LC filters affects the dynamics of rotor-flux-oriented controlled induction motor drive with the inverter output filter. Therefore, a combined control technique has been proposed in this paper, where the state variables of the filter and motor have been controlled using the state-feedback controller and proportional-integral controllers, respectively. The proposed technique has been analyzed and simulation results have been provided for validation.

Published
2021

39. Comparison of Finite Element Modelling Methods for Power Magnetic Components

Author

Thomas Ebel, Wulf-Toke Franke, and Boyan Dinev

Subjects
Computer science, Finite-Element Method, Solid modeling, Inductor, Accuracy comparison, Modelling, Finite element method, Power (physics), Magnetics, Power electronics, Electronic engineering, Electronics, Ansys Electronics, Design methods, Electrical impedance
Abstract

The paper compares the performance of different design methods for power magnetics. The Finite element method solvers provided by ANSYS Electronics are compared to the theoretical magnetic models. The performance of the modelling tools is quantified by comparing simulations against impedance measurements for 2 different inductor types both for 2D approximations and 3D geometry models. The key goal is to evaluate when a more complex design procedure is required to achieve certain design goals, such as accurate parasitic and be aware of the limitations in different methods.

Published
2021
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45. Comparative investigation of body diode reliability of 1,2-kV SiC power switches for the temperature measurement

Author

Giorgo Kapino, Wulf-Toke Franke, Thomas Ebel, Walid Mansour, and Jianzhi Fu

Subjects
Materials science, business.industry, chemistry.chemical_compound, Reliability (semiconductor), Body-diode, chemistry, Bipolar degradation, MOSFET, Silicon carbide, Power cycling, Optoelectronics, SiC MOSFET, Junction temperature, Power semiconductor device, business, Voltage drop, Diode
Abstract

In this paper, the body diode’s reliability of the commercial 1,2 kV SiC power transistors is investigated. The body-diode voltage drop methodology (V SD -T methodology) is considered the most reliable temperature sensitive electrical parameter (TSEP) for the junction temperature estimation of SiC MOSFETs during the process of Power Cycling Test (PCT). Literature shows the applied source-drain sensing current would also cause bipolar degradation (BD). The purpose of this article is to demonstrate a PCT-related bipolar degradation phenomenon, the results are comparatively analyzed in commercially available 1,2kV SiC MOSFETs with different structures.

Published
2021
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50. Destination Prediction Based on Partial Trajectory Data

Author

Ibrahim Emre Göl, Christoph Lingenfelder, Patrick Ebel, and Andreas Vogelsang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Mean squared error, Computer Science - Artificial Intelligence, Computer science, Machine Learning (stat.ML), 02 engineering and technology, computer.software_genre, Machine Learning (cs.LG), Contextual design, Statistics - Machine Learning, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, 050210 logistics & transportation, Artificial neural network, business.industry, 05 social sciences, Navigation system, I.2.1, Tree (data structure), Artificial Intelligence (cs.AI), Recurrent neural network, Global Positioning System, Trajectory, 020201 artificial intelligence & image processing, Data mining, business, computer
Abstract

Two-thirds of the people who buy a new car prefer to use a substitute instead of the built-in navigation system. However, for many applications, knowledge about a user's intended destination and route is crucial. For example, suggestions for available parking spots close to the destination can be made or ride-sharing opportunities along the route are facilitated. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data. The approach follows a three-step procedure: First, a $k$-d tree-based space discretization is performed, mapping GPS locations to discrete regions. Secondly, a recurrent neural network is trained to predict the destination based on partial sequences of trajectories. The neural network produces destination scores, signifying the probability of each region being the destination. Finally, the routes to the most probable destinations are calculated. To evaluate the method, we compare multiple neural architectures and present the experimental results of the destination prediction. The experiments are based on two public datasets of non-personalized, timestamped GPS locations of taxi trips. The best performing models were able to predict the destination of a vehicle with a mean error of 1.3 km and 1.43 km respectively., Comment: 2020 IEEE Intelligent Vehicles Symposium

Published
2020
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