Dr Victor Mazzilli

Postgraduate research student


Victor Mazzilli, Stefano De Pinto, Leonardo Pascali, Michele Contrino, Francesco Bottiglione, Giacomo Mantriota, Patrick Gruber, Aldo Sorniotti (2021)Integrated chassis control: Classification, analysis and future trends, In: Annual Reviews in Control Elsevier Ltd

Integrated Chassis Control (ICC) is one of the most appealing subjects for vehicle dynamics specialists and researchers, due to the increasing number of chassis actuators of modern human-driven and automated cars. ICC ensures that the potential of the available actuators is systematically exploited, by overcoming the individual limitations, and solving conflicts and redundancies, which results into enhanced vehicle performance, ride comfort and safety. This paper is a literature review on ICC, and focuses on the topics that are left uncovered by the most recent surveys on the subject, or that are dealt with only by old surveys, namely: a) the systematic categorisation of the available ICC architectures, with the critical analysis of their strengths and weaknesses; b) the latest ICC approaches, which are becoming feasible with modern automotive microcontrollers; c) the driving performance requirements; and d) the procedures to objectively evaluate ICC performance. The manuscript aids the interested reader in the choice of the most appropriate ICC method for the specific requirements, and concludes with the recent developments and future trends.

V. Mazzilli, D. Ivone, V. Vidal Muñoz, S. De Pinto, P. Camocardi, L. Pascali, A. Doria Cerezo, P. Gruber, G. Tarquinio, A. Sorniotti (2020)On the vehicle state estimation benefits of smart tires, In: Proceedings of chassis.tech plus 2020 - the 11th International Munich Chassis Symposium

Smart tires are systems that are able to measure temperature, inflation pressure, footprint dimensions, and, importantly, tire contact forces. The integration of this additional information with the signals ob-tained from more conventional vehicle sensors, e.g., inertial measure-ment units, can enhance state estimation in production cars. This paper evaluates the use of smart tires to improve the estimation performance of an Unscented Kalman filter (UKF) based on a nonlinear vehicle dynam-ics model. Two UKF implementations, excluding and including smart tire information, are compared in terms of estimation accuracy of vehicle speed, sideslip angle and tire-road friction coefficient, using experi-mental data obtained on a high performance passenger car.