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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.
For a low voltage IPMSM used in a hybrid drive system of a consumer car, it is of the highest importance to design a torque controller circuit that produces an accurate torque at the shaft. The accurate torque is needed to distribute the load between the combustion engine, or the manual break, and the electrical drive. As the capacitance of the batteries used in this type of car is usually very small, the control of the batteris state of charge and its output current is quite critical. Therefore, a precise torque control is elementary. Temperature changes have a large impact on the IPMSM internal parameters. Especially the permanent magnet flux and the stator resistance are affected by temperature changes. There are techniques to observe and calculate the temperature variation of these parameters. This contribution describes a method to handle the influence of temperature variation on the actual torque at the shaft, by correcting the current commands of the open loop controller.
Future electricity flows and their impact on the power distribution grid on a decentralized level
(2022)
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.
In this presentation the actual state of the ongoing investigations of spray painting processes are summarized. Already at the end of the last century, first experimental and numerical work was done aiming to estimate the film thickness distribution and the transfer efficiency of spray painting processes of complex workpieces. First successful numerical simulations basically used the classical Euler-Langrange approach, in case of electrostatically supported atomizers extended by the necessary models for the charging of droplets and the interaction between charged droplets and electrical fields. Measured droplet size distributions and velocities near the atomizers have been applied as inlet conditions for the simulations.
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.
Application of Induction Thermography for Detection of Near Surface Defects in Steel Products
(2020)
HElmar-LiMo 2040
(2023)
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.
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.