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Nowadays, new technologies are pushing the road vehicle limits further. Promising applications, e.g., self-driving cars, require a suitable control system that can maintain the vehicle’s stability in critical scenarios. In most of current cars, the control systems actuates independently, meaning there is not a coordination or data sharing between them. This approach can produce a conflict between these standalone controllers and thus, no improvements on the vehicle’s stability are achieved or even a worse scenario can be generated. In order to overcome these problems, an integrated approach is developed in this work. This integration, defined in this work as Integrated Control (IC), is done by an intelligence coordination of all standalone controllers inside the vehicle, i.e., Anti-Lock Braking System (ABS), Electronic Stability Program (ESP) and Four-Wheel Steering System (4WS). The ABS model was built using Fuzzy logic, for which only three rules were necessary to get a good performance. To design the ESP and the 4WS, the simple handling vehicle model was used as a reference behavior. The IC was designed using the hierarchical approach with two layers, i.e., the upper and lower layer. The upper one, observes the side slip angle and depends of its value the upper layer triggers the ESP or the 4WS. Finally, in order to prove the improvements of the IC system over the non-integrated approach, a full-size vehicle model was used to perform simulation in run-off-road and μ-split scenarios.
Nach dem Aufbau eines Mehrkörper-Simulationsmodells muss dieses auf Richtigkeit, Funktionalität und Wirtschaftlichkeit getestet werden. Die Ermittlung der Gleichgewichtslage stellt dabei eine erste Plausibilitäts-Kontrolle dar. Eine Linearisierung mit anschließender Analyse der Eigendynamik liefert Aussagen über die Frequenzen und das Dämpfungsverhalten des Modells. Einfache Erregersignale ermöglichen einen ersten Einblick in das nichtlineare dynamische Verhalten des Modells. Modell-Parameter, die nicht genau bekannt sind, können durch gezielte Variationen plausibel geschätzt oder über eine Optimierung sogar mit optimalen Werten belegt werden. Nach all diesen Tests steht das Mehrkörper- Simulationsmodell dann für praktischeUntersuchungen zurVerfügung, die neben reinen Zeitsimulationen auch Methoden der Inversen Kinematik und der Inversen Dynamik mit einschließen.
Virtual testing procedures have become a standard in vehicle dynamics. The increasing complexity of driver assistance sys-
tems demand for more and more virtual tests, which are supposed to produce reliable results even in the limit range. As a consequence,
simplified vehicle models, like the classical bicycle model or 4-wheel vehicle models, have to be replaced by a fully three-dimensional
and nonlinear vehicle model, which also encompasses the details of the suspension systems. This paper presents a passenger car model,
where the chassis, the four knuckles, and the four wheels are described by rigid bodies, the suspension system is modeled by the generic
design kinematics, and the TMeasy tire model provides the tire forces and torques in all driving situations.
Friction has long been an important issue in multibody dynamics. Static friction models apply appropriate regularization techniques to convert the stick inequality and the non-smooth stick–slip transition of Coulomb’s approach into a continuous and smooth function of the sliding velocity. However, a regularized friction force is not able to maintain long-term stick. That is why dynamic friction models were developed in recent decades. The friction force depends herein not only on the sliding velocity but also on internal states. The probably best-known representative, the LuGre friction model, is based on a fictitious bristle but realizes a too-simple approximation. The recently published second-order dynamic friction model describes the dynamics of a fictitious bristle more accurately. It is based on a regularized friction force characteristic, which is continuous and smooth but can maintain long-term stick due to an appropriate shift in the regularization. Its performance is compared here to stick–slip friction models, developed and launched not long ago by commercial multibody software packages. The results obtained by a virtual friction test-bench and by a more practical festoon cable system are very promising. Thus, the second-order dynamic friction model may serve not only as an alternative to the LuGre model but also to commercial stick–slip models.
Friction has long been an important issue in multibody dynamics. Static friction models apply appropriate regularization techniques to convert the stick inequality and the non-smooth stick-slip transition of Coulomb’s approach into a continuous and smooth function of the sliding velocity. However, a regularized friction force is not able to maintain long-term stick. That is why, dynamic friction models were developed in the last decades. The friction force depends herein not
only on the sliding velocity but also on internal states. The probably best known representative, the LuGre friction model, is based on a fictitious bristle but realizes a too simple approximation. The recently published second order dynamic friction model describes the dynamics of a fictitious bristle more accurately. Its performance is compared here to stick-slip friction models, developed and launched not long ago by commercial multibody software packages.
n the automotive industry, simulations are needed to analyse the dynamics of vehicles and also of its main components and subsystems, e.g. tires, brakes and suspension systems. These simulations are required for an early-stage development and in consequence, they must deliver realistic results. Suspension systems plays a key role in comfort and safety of road vehicles. They usually consist of rigid links and force elements that are arranged with a specific topology. In addition, some of their functionalities are to carry the weight of the car and the passengers, and maintain a correct wheel alignment. In simulations involving suspension systems, lookup-tables are frequently used. They are obtained from a Kinematic and Compliance (KnC) test and then standardized for a specific vehicle simulation software. Nonetheless, lookup-tables require a reasonable number of characteristic points. Additionally, derivatives, interpolation, and extrapolation are not necessarily smooth. This produces results that depend on the interpolation technique and may be inaccurate. In this paper, a novel method called “design kinematics” is proposed. This method can describe the kinematic properties of almost any type of suspension systems. Comparisons with an analytic calculation and a KnC measurement shown that the design kinematics is able to represent the kinematic and compliance properties of suspension systems extremely well and very efficiently.