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Masterarbeiten (Details und Kontaktinformationen: siehe unten):
- Prädiktion der Drehbewegungen eines Autositzes
- An Integrated Estimator for Online Vehicle Mass and Road Slope based on IMU
- Real-time Estimation the Position of Vehicle Center of Gravity based on IMU
- Steering Feedback Torque Computation for Steer-by-Wire System Based on Artificial Neural Network
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Zur Bearbeitung von Abschlussarbeiten bitte die folgenden Hinweise pdf beachten.
| Thema | Prädiktion der Drehbewegungen eines Autositzes pdf |
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| Beschreibung | Mit der zunehmenden Automatisierung von Kraftfahrzeugen wird der Fahrer immer mehr selber zum Passagier. Hierdurch rücken alternative Sitzkonzepte und die Möglichkeit, nicht fahrgebundene Aufgaben erledigen zu können, in den Fokus. Eine negative Begleiterscheinung wird eine höhere Anfälligkeit für Kinetose (auch Reise- oder Bewegungskrankheit) sein. Damit beschreibt man die Neigung, bei bestimmten Bewegungen während einer Fahrt mit einem Fahrzeug mit Unwohlsein, Übelkeit bis hin zum Brechreiz zu reagieren. Beifahrer sind gegenüber aktiven Fahrern davon sehr viel stärker betroffen. Im Rahmen eines DFG-Projekts wird am Fachgebiet Kraftfahrzeuge der TU Berlin untersucht, inwieweit eine Vermeidung von Kinetose bei automatisiert fahrenden Kraftfahrzeugen möglich ist. Hierzu wird ein aktiv bewegter Sitz (Motion Seat) entwickelt, der Drehbewegungen des Fahrzeugs ausgleichen und somit Kinetose verhindern soll. Zur Optimierung der Regelung soll überprüft werden, inwieweit die Drehbewegungen des Sitzes mithilfe von vorherigen und aktuellen fahrdynamischen Größen prädiktiv geschätzt werden können. Hierbei sollen verschiedene Modellansätze implementiert, ausgewertet und optimiert werden. |
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| Ansprechpartner/in | Andreas Hartmann, M. Sc. |
| Thema | An Integrated Estimator for Online Vehicle Mass and Road Slope based on IMU pdf |
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| Beschreibung | A significant number of mass estimation algorithms have been developed with longitudinal dynamics. However, most of these approaches are based on the method of constant vehicle mass and time-varying road grade. Although both vehicle mass and road grad could be identified, the nature of time-varying road grade could lead to significant disturbance for the precision of vehicle mass estimation. In addition, parameters including rolling resistance, drag coefficient and wind velocity are necessary, which also should be estimated. Consideration of the limitations about estimation approaches, a novel approach will be proposed in this task. In order to decouple the coupled influence of road grade on vehicle mass estimation, this task proposed a novel method for vehicle mass estimation based on frequency-information-extraction. Figure 1 shows that the principle of the MTWFFT method. Normally, the dynamic signals are directly obtained from measurements in the time domain. This task adopts vertical acceleration and angular velocity from IMU measurement which treats the vertical acceleration of the body mass as inputs in the dynamic equations. The estimation approach is based on the observation that the frequency spectrum of the both the vertical accelerometer and the motions angular rate significantly varies as a function of the vehicle loading mass and its distribution. This can be indicated by the instruction in Fig. 2, which shows the ratio index and the accelerometer spectra obtained where the same vehicle is facing the same road profile, but with different loading mass, located in the same position inside the vehicle. |
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| Ansprechpartner/in | Xiongshi Wang, M. Sc. |
| Thema | Real-time Estimation the Position of Vehicle Center of Gravity based on IMU pdf |
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| Beschreibung | The position of a vehicle’s center of gravity (CoG) are used as an important parameter for vehicle safety control systems for improving handling stability, while it can be changed considerably according to various driving conditions. Therefore, in order to make vehicle safety control systems to have the better performance, it is essential to obtain the accurate CoG position. However, it is generally difficult to acquire the value of this parameter directly through sensors due to cost reasons. In this task, a practical algorithm for estimating vehicle’s CoG position in real time will be proposed. This algorithm is derived only based on pitch and roll movements of the vehicle. Figure 1, Vehicle dynamics model with roll and pitch movements. Moreover, the main differences in the proposed algorithm compared to previous studies is that it does not require information such as vehicle mass, vehicle moments of inertia, road grade or tire-road surface friction, which are difficult to acquire. In the proposed algorithm, the relationship between the tire vertical force and the corresponding Pitch&Roll angles are used to determine the CoG position. To demonstrate a practical use of the proposed algorithm, the tire vertical force distribution will be tested under variable loading position and payloads. The proposed CoG estimation algorithm and its practical use will be verified via simulations and experiments with using a test vehicle equipped with Inertial Measurement Unit (IMU). |
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| Ansprechpartner | Xiongshi Wang, M. Sc. |
| Thema | Steering Feedback Torque Computation for Steer-by-Wire System Based on Artificial Neural Network PDF |
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| Beschreibung | Steer-by-Wire (SbW) is a highly prospective steering technology for intelligent vehicles. As the elimination of the mechanical connection between the steering wheel and the steered wheels, it becomes free to adjust steering characteristics for an SbW system, which contributes to improving steering sensitivity, steering stability, and handling performance. On the other hand, how to generate a proper artificial steering feedback torque comes to be a vital and challenging issue. The target of this research is to design a new method to calculate the desired steering feedback torque which provides the driver with a realistic steering feel, the same as in an electrical power steering (EPS) system. For this purpose, an artificial neural network (ANN, shown in Fig. 1) is adopted to estimate the steering feedback torque, as ANN is capable of learning complex non-linear correlations without requiring specific mathematical models. The development of this ANN requires certain steps. Firstly, piles of steering data are recorded by imposing various steering maneuvers in IPG CarMaker to the vehicle simulator coupled with the well-performed EPS steering testbench (Fig. 2) which is available in our Department Automotive Engineering. Secondly, the inputs of ANN are selected by parameter sensitivity analysis of the test data. Besides, the training and validation of this ANN are conducted based on the steering database. Overall, a proper approximation of the steering feedback torque for SbW system can be developed, which provides the driver with a similar feeling as an EPS system. |
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| Ansprechpartner | Qiao Zhang, M.Sc. |
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