|Tipo di tesi||Tesi di laurea magistrale|
|Titolo||R-EPS model development for steering feeling characterization of a high performance car|
|Titolo in inglese||R-EPS model development for steering feeling characterization of a high performance car|
|Struttura||Dipartimento di Ingegneria "Enzo Ferrari"|
|Corso di studi||Advanced Automotive Engineering (D.M.270/04)|
|Data inizio appello||2020-10-22|
|Disponibilità||Embargo di 3 anni|
|Data di rilascio||2023-10-22|
The following master's thesis work is the result of the internship activity performed in Maserati S.p.A., specifically in Steering System division of BCI Components department.
The following master's thesis work is the result of the internship activity performed in Maserati S.p.A., specifically in Steering System division of BCI Components department. The objective is the development and improvement of a rack electric power steering system model to be used in cosimulation with full vehicle model. The model under study results of critical importance for the definition of the most relevant parameters for a calibration file of a R-EPS, regarding mechanical components and, hence, useful during the initial phase of vehicle and steering system development to provide suitable specifications to supplier. A further goal of the present work is the research of a correlation between objective vehicle dynamics quantities and subjective criteria belonging to steering feeling area. The R-EPS model is designed on Matlab/Simulink to be used in cosimulation with full vehicle model of VI-CarRealTime. A discrete degree of correlation with actual physical steering system is researched through the analysis of different data obtained from experimental characterization of components at test bench. Subsequent activity is focused on an offline sensitivity analysis of delay parameters in frequency domain, which represents the starting point for a further DoE carried out at the dynamic simulator with professional driver to investigate the effects of parameter modifications on driver's subjective assessment. The final version of the model has allowed to obtain a discrete degree of correlation with actual steering system. The sensitivity analysis and dynamic simulator activity have allowed the definition of a direction of preference for the majority of investigated parameters in order to improve driver’s steering feeling. Furthermore, the most relevant parameters in frequency domain for driver's subjective assessment have been identified, along with the track turns which seem to strongly affect steering criteria. The findings of the following work might represent the starting point for the setup of a virtual estimator, using neural network, to predict subjective evaluation on a specific car model and road track.