Feedback through the steering wheel is known as the most important source of information to the driver. The so-called steering feeling, composed of self-aligning actions coming from tyres and suspension geometry all the way through mechanical linkages to the driver’s hands, provides vital communication for intuitive driving, and it is therefore utterly important for safety and for a pleasant driving experience as well. Subtle forces and vibrations, due to the interaction between the tyre contact patch and the road surface texture, also play a role, provided they are not heavily filtered or cancelled by the power steering system. Human perception is guided by experience in order to establish correlations between steering feedback and vehicle motion in terms of straight-line stability, cornering speed, tyre adhesion and available friction, vehicle balance, and so on. A front-wheel drive car is potentially a critical vehicle from this point of view, especially when the powertrain can deliver large torque figures, and even more so if a limited-slip differential (LSD) or a similar active device is present in order to improve traction capabilities. Any difference between the two wheels in terms of tractive force can result into the so-called torque steer issue, that is to say, a “pulling” sensation on the steering wheel or a shifting of the vehicle from the desired trajectory. This paper analyses the torque steer phenomenon on an all-wheel-drive, full electric sportscar where a significant portion of the torque is transferred to the front axle. The effects of suspension kinematics and the load variation at tyre contact patch level are taken into account. For evaluating the impact of steering feedback, the VI-grade® simulation software is adopted and a test campaign on the professional driving simulator available at the University of Brescia has been carried out in order to understand the impact of steering feedback on driver perception and performance.
The so-called porpoising is a well-known problem similar to bouncing that is affecting the dynamic behavior of basically all the field of 2022 Formula 1 racing cars. It is due to the extreme sensitivity of aerodynamic loads to ride height variations along a lap. Mid-way through the season race engineers are still struggling to cope with this phenomenon and its consequences, with regard to either physiological stress experienced by the drivers or to overall vehicle performance and stability. The paper introduces two kinds of models based on real-world chassis and aerodynamic data, where the above-mentioned downforce sensitivity has been arbitrarily recreated through the application of a decay function to aero maps. The first one is a quasi-static model, usually adopted as a trackside tool for controlling ride heights and aero balance, while the second, a fully dynamic model, recreates the interaction between oscillating aerodynamic loads and suspension dynamics resulting in a visible porpoising phenomenon. Basic setup changes have been tested, including significant static ride height variations. The paper should be seen as a proposal of guidelines in the search of a trade-off between aerodynamic stability and overall performance, without pretention of quantitative accuracy due to the highly confidential topic, which makes numerical validation impossible.
Vehicle sideslip angle estimation is still one of the most challenging research topics in the automotive industry. Many papers can be found on this topic, where authors propose varied methods to reach the goal. Which is the most effective? After an extensive literature review, two very different methods have been identified as the most used: Extended Kalman Filter with dynamic model and Artificial Neural Network. In this work a comparison among these methods is presented. A fully instrumented car has been used to gather typical vehicle dynamics data and feed the models required for a model-based design approach. Results showed that each method has either positive aspects or drawbacks.
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