Alessandro Scamarcio

Mr Alessandro Scamarcio

My publications


Alessandro Scamarcio, Mathias Metzler, Patrick Gruber, Aldo Sorniotti (2019)Influence of the prediction model complexity on the performance of model predictive anti-jerk control for on-board electric powertrains, In: Proceedings of the 26th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD 2019) CRC Press

Anti-jerk controllers compensate for the torsional oscillations of automotive drivetrains, caused by swift variations of the traction torque. In the literature model predictive control (MPC) technology has been applied to anti-jerk control problems, by using a variety of prediction models. However, an analysis of the influence of the prediction model complexity on anti-jerk control performance is still missing. To cover the gap, this study proposes six anti-jerk MPC formulations, which are based on different prediction models and are fine-tuned through a unified optimization routine. Their performance is assessed over multiple tip-in and tip-out maneuvers by means of an objective indicator. Results show that: i) low number of prediction steps and short discretization time provide the best performance in the considered nominal tip-in test; ii) the consideration of the drivetrain backlash in the prediction model is beneficial in all test cases; iii) the inclusion of tire slip formulations makes the system more robust with respect to vehicle speed variations and enhances the vehicle behavior in tip-out tests; however, it deteriorates performance in the other scenarios; and iv) the inclusion of a simplified tire relaxation formulation does not bring any particular benefit.

Mathias Metzler, Alessandro Scamarcio, Patrick Gruber, Aldo Sorniotti (2019)Real-time capable nonlinear model predictive wheel slip control for combined driving and cornering, In: Proceedings of the 26th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD 2019) Springer

This paper presents a traction controller for combined driving and cornering conditions, based on explicit nonlinear model predictive control. The prediction model includes a nonlinear tire force model using a simplified version of the Pacejka Magic Formula, incorporating the effect of combined longitudinal and lateral slips. Simulations of a front-wheel-drive electric vehicle with multiple motors highlight the benefits of the proposed formulation with respect to a controller with a tire model for pure longitudinal slip. Objective performance indicators provide a performance assessment in traction control scenarios.

Alessandro Scamarcio, Patrick Gruber, Stefano De Pinto, Aldo Sorniotti (2020)Anti-jerk controllers for automotive applications: A review, In: Annual Reviews in Control Elsevier

Anti-jerk controllers, commonly implemented in production vehicles, reduce the longitudinal acceleration oscillations transmitted to the passengers, which are caused by the torsional dynamics of the drivetrain during torque transients. Hence, these controllers enhance comfort, drivability, and drivetrain compo- nent durability. Although anti-jerk controllers are commonly implemented in conventional production internal-combustion-engine-driven vehicles, the topic of anti-jerk control has recently been the subject of increased academic and industrial interest, because of the trend towards powertrain electrification, and the distinctive features of electric powertrains, such as the high torque generation bandwidth and absence of clutch dampers. This paper reviews the state-of-the-art of automotive anti-jerk control, with particular attention to control structures that are practically implementable on real vehicles. The survey starts with an overview of the causes of the longitudinal vehicle acceleration oscillations that follow abrupt changes in the powertrain torque delivery. The main body of the text reviews examples of anti-jerk controllers, and categorizes them according to the adopted error variable. The ancillary functions of typical anti-jerk controllers, e.g., their activation and deactivation conditions, are explained. The paper concludes with the most recent development trends, and ideas for future work, including possible applications of model pre- dictive control as well as integration of anti-jerk controllers with autonomous driving systems and other vehicle control functions.

Alessandro Scamarcio, Mathias Metzler, Stefano De Pinto, Aldo Sorniotti (2020)Comparison of Anti-Jerk Controllers for Electric Vehicles With On-Board Motors, In: IEEE Transactions on Vehicular Technology Institute of Electrical and Electronics Engineers

Anti-jerk controllers actively suppress the torsional oscillations of automotive drivetrains, caused by abrupt variations of the traction torque. The main benefits are: i) enhanced passengers’ comfort; and ii) increased component life. Extensive literature deals with the design of anti-jerk controllers for electric powertrains with on-boardmotors, i.e., in which the electricmotor is part of the sprung mass of the vehicle, and transmits torque to the wheels through a transmission, half-shafts and constant velocity joints. Nevertheless, a complete and structured comparison of the performance of the different control options is still missing. This study addresses the gap through the assessment of six anti-jerk controllers – five exemplary formulations from the literature, and one novel formulation based on explicit nonlinearmodel predictive control (eNMPC). All proposed control structures have the potential to be implemented on production vehicles. A set of objective performance indicators is defined to assess the controllers, which are tuned through an optimization-based routine.Results showthat the wheel speed input is critical to enhance controller performance, but may lead to reduced robustness.