Napolitano Dell'Annunziata, Guido (2023) Vehicle Performance Analysis & Multi-Physical Optimization based on Innovative KPIs. [Tesi di dottorato]

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Abstract

Understanding and optimizing vehicle performance is a constant challenge in the motorsport environment. In recent years, a lot of effort has been spent developing models able to describe the complex phenomena that occur during races. There are many different factors that affect the performance of a race car, but they can be synthesized into three macro-areas: (1) the vehicle itself with its subsystems, (2) the driver and (3) the external conditions, such as environmental conditions and road roughness. This Ph.D. path has been developed with the aim of combining all these relevant factors, exploiting the research outcoming of the last years in all the different areas explored within this work, providing new methodologies to understand vehicle behavior and offering effective procedures to optimize performance. All the activities presented are based on a large amount of experimental data acquired on track during dedicated test sessions or races; this approach has been chosen to investigate the real operating conditions, hardly reproducible with accuracy on test benches. First of all, two different tools have been developed to characterize, respectively, the tire road interaction and vertical ride. The first one, the T.R.I.C.K. 2.0 tool can evaluate interaction forces and slip indices starting from the acquired data on track. The second, that is Virtual 7-Post Tool, is able to reproduce faithfully the suspension behaviors giving useful suggestions on how to modify the setup to reach the desired functioning of the wheel group. Then, a methodology to characterize drivers’ skills is presented. Starting from data collected from different drivers, on the same vehicle and on the same track, several objective and generalized metrics have been defined to classify their capacity. The procedure developed can be applied in other fields giving useful suggestions about which are the weaknesses of different drivers, defining specific strategies to refine their abilities. After that, the other impacting factors are explored, with a specific focus on tire viscoelastic properties evaluation and road roughness characterization. For both these topics, new acquisition methods and processing procedures are presented and new KPIs with their effectiveness are illustrated. In the last part of the thesis, a global case study, for which are available all the typology of data previously described, is exposed; after the organization of the dataset, carried out with different methods of data reduction, the influence of the different KPIs on the tire grip and wear is analyzed. In this phase, some techniques have been built in order to find correlations among all these parameters and to highlight mutual dependencies. Thanks to these analyses, it has been possible to find the optimal tire thermal working range, the effect of the roughness on the grip and the possibility to predict the wear, knowing few relevant information. The obtained results confirm the validity of the chosen approaches, giving a substantial contribution to vehicle behavior overall comprehension and resulting in effective procedures usable to maximize motorsport team performance on track.

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