Prediction of Brake System Performance during Race Track/High Energy Driving Conditions with Integrated Vehicle Dynamics and Neural-Network Subsystem Models

摘要

In racetrack conditions, brake systems are subjected to extreme energy loads and energy load distributions. This can lead to very high friction surface temperatures, especially on the brake corner that operates, for a given track, with the most available traction and the highest energy loading. Individual brake corners can be stressed to the point of extreme fade and lining wear, and the resultant degradation in brake corner performance can affect the performance of the entire brake system, causing significant changes in pedal feel, brake balance, and brake lining life. It is therefore important in high-performance brake system design to ensure favorable operating conditions for the selected brake corner components under the full range of conditions that the intended vehicle application will place them under. To address this task in an early design stage, it is helpful to use brake system modeling tools to analyze system performance. Traditional modeling approaches have relied upon simple mathematical representation of measured brake corner output and fluid displacement (data which often result from inertia dyno testing) to predict system-level performance. Many brake system analysis tools also include only a quasi-static, two-dimensional vehicle dynamics model and do not fully capture cornering influences on braking energy load distribution. This paper will present a method of measuring brake corner output and compliance behavior during high energy usage conditions on a brake dynamometer and representing this behavior with neural network models. The resulting models are integrated with a fully 3-dimensional vehicle dynamics and 1-dimensional brake thermal model to result in significantly more accurate brake system performance predictions that include the vehicle dynamics influences on brake corner energy loading, brake corner responses such as in-stop fade and recovery, and in-stop lining wear and its influence on compliance. The resultant integrated neural-network brake corner and vehicle dynamics model is demonstrated in both straight-line braking events and simple race track simulations.