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Systematic and user-oriented development of physical interface for vehicle ultrafast charging
(2022)
Designing the OR cockpit
(2021)
Optimization of EMI filter with consideration of the noise source impedance for DC/DC converter
(2021)
Coverage Probability of Methods for Steady-State Availability Inference with a Confidence Interval
(2021)
Application of Induction Thermography for Detection of Near Surface Defects in Steel Products
(2020)
This paper describes the modelling, simulation and energy management of a fuel cell hybrid heavy-duty truck. For this purpose, a longitudinal dynamic model of a 26t truck was set up and the load requirement for the drive train was determined based on a driving cycle. To meet this load requirement as efficiently and dynamically as possible three different energy management strategies were implemented, tested and the impact on the overall system was analysed. In addition, the behaviour of the hybrid system with the various energy management strategies with different battery capacity is shown and analysed.
This paper provides an analysis of the trend in autonomous driving traffic and the development of infrastructural support, whereas the requirements on the infrastructural support will be analyzed. Then selected traffic scenes will be implemented in an autonomous driving simulator tool in order to figure out the required parameters to assist the autonomous vehicle from the infrastructure.
For an efficient operation of a low voltage PMSM an optimized voltage usage is very important. Because of the relation between the low voltage and the high currents in this type of machine, a large voltage reserve is needed to compensate the influence of parameter mismatches and to guarantee a stable current control. As the power is limited by the low voltage in this type of hybrid drive systems, optimizing the voltage usage is also required to maximize the power and the torque availability. This paper describes a closed loop flux control to maximize the voltage usage. The controller feedback is used to estimate and maximize the available torque for each operating point.
Die vorliegende Ausführung beschreibt Methoden zur Ermittlung kritischer Interaktionen bei der ÖPNV-Nutzung. Mithilfe einer generischen Analyse einer typischen ÖPNV-Nutzung und einer explorativen Beobachtungsstudie an einer Bushaltestelle werden besonders kritische Interaktionen identifiziert und verifiziert.
OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections
(2020)
This paper presents the Online Adaptive Fuser: OAFuser, a novel method for online adaptive estimation of motion and measurement uncertainties for efficient tracking and fusion by applying a system of several estimators for ongoing noise along with the conventional state and state covariance estimation. In our system, process and measurement noises are estimated with steady-state filters to obtain combined measurement noise and process noise estimators for all sensors in order to obtain state estimation with a linear Minimum Mean Square Error (MMSE) estimator and accelerating the system’s performance. The proposed adaptive tracking and fusion system was tested based on high fidelity simulation data and several real-world scenarios for automotive radar, where ground truth data is available for evaluation. We demonstrate the proposed method’s accuracy and efficiency in a challenging, highly dynamic scenario where our system is benchmarked with Multiple Model filter in terms of error statistics and run time performance.
Application and machine learning methods for dynamic load point controls of electric vehicles (xEVs)
(2020)
From the customer's perspective, the appeal of electric vehicles depends on the simplicity and ease of their use, such as flexible access to electric power from the grid to recharge the batteries of their vehicles. Therefore, the expansion of charging infrastructure will be an important part of electric mobility. The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs. This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques. The module will be integrated into a real charge management concept with optimization procedures for controlling the dynamic load point. The value of load forecasting through practical load data of a car park were taken to illustrate the proposed methods. The prediction performance of different ML methods under the same data condition (e.g., holiday data) are compared and evaluated.
Negotiations are a relevant and highly complex business skill. Therefore, extensive training is required to become a good negotiator. Such training is offered by universities for their students and by companies for their employees. The present paper designs gamified feedback features in electronic negotiation training and evaluates their potential and their effects. Following a design science research method, feedback mechanisms in electronic negotiation training are derived from literature. An assessment regarding their relevance for e-negotiation training shows a preparation quiz, set and track goals and expert reviews to be the most useful gamified feedback mechanisms. Dedicated mock-ups implementing these feedback mechanisms are designed and evaluated in semi-structured interviews showing their capability to improve relevant negotiation skills, as well as motivation and competence of the learners. Out of the three mock-ups, the interviewees prefer the feedback mechanisms “expert review” and “set and track goals”; both mechanisms provide a competence-confirming learning experience and an autonomous learning experience.