Optimization is a key tool used by automakers to efficiently design and manufacture vehicles. During vehicle design, much effort is devoted to efficiently simulate and optimize as many vehicle parameters as possible to save development costs during physical testing. One area of vehicle development that heavily relies on physical testing and subjective driver feedback is the tire design process. Optimizing tire parameters relies either on this subjective feedback from trained drivers, or use of existing tire data or scaling of a reference tire model simulate the desired design change and provide feedback. These data are often difficult to obtain and properly scale to represent the appropriate design changes. Michelin's TameTire model is a force and moment tire model. It includes thermal tire effects and is physically derived, thereby allowing quick access to scaling factors to change a tire's behavior based on pertinent tire design changes such as tread depth and tread stiffness. In this paper, a multi-objective optimization is performed to observe the trade-off between tire wear and handling performance by using the scaling factors available in the TameTire model.

You do not currently have access to this content.