Riassunto analitico
The following work presents a high-performance yet cost-conscious methodology for estimating lateral slip angles using a highly constrained dataset, thereby eliminating the need for dedicated slip angle sensors. The approach is applied to the Dallara DW12 IndyCar, relying on just six data channels.
The study begins by introducing the Linear Single Track Model (STM), also known as the Bicycle Model, along with transient dynamics extensions that establish the theoretical and mathematical foundation for the derived telemetry-based estimations.
The computational framework is structured into two iterative loops. Loop 1 implements an initial estimation model based on core equations, formulated through a series of MATLAB scripts and functions. The results of this implementation undergo a rigorous correlation analysis against real-world slip angle sensor data to evaluate accuracy. Loop 2 expands upon this framework by incorporating a more comprehensive dynamics, solved via ordinary differential equations (ODEs). The results of Loop 2 are likewise subjected to detailed correlation analysis. A comparative value-matrix evaluation is then performed to identify the most effective code modules and functions for optimal accuracy and computational efficiency.
Given the substantial scatter observed, a systematic investigation is conducted to examine the underlying causes of discrepancies between model predictions and directly measured values. This study critically assesses the limitations of the proposed dynamic modeling approach. The thesis concludes by outlining the key steps required to transition this framework from a proof-of-concept to a fully operational tool for race engineering applications.
The findings demonstrate that while lateral slip angles can be estimated with reasonable accuracy using a limited set of telemetry channels, a simplistic STM-based approach is insufficient for robust and reliable results.
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