Torque Vectoring Control for fully electric Formula SAE cars

DE PASCALE, Valentina, LENZO, Basilio, FARRONI, Flavio, TIMPONE, Francesco and ZHANG, Xudong (2020). Torque Vectoring Control for fully electric Formula SAE cars. In: Proceedings of XXIV AIMETA Conference 2019. Lecture Notes in Mechanical Engineering . Springer, 1075-1083.

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Abstract

Fully electric vehicles with individually controlled powertrains can achieve significantly enhanced vehicle response, in particular by means of Torque Vectoring Control (TVC). This paper presents a TVC strategy for a Formula SAE (FSAE) fully electric vehicle, the “T-ONE” car designed by “UninaCorse E-team” of the University of Naples Federico II, featuring four in-wheel motors. A Matlab-Simulink double-track vehicle model is implemented, featuring non-linear (Pacejka) tyres. The TVC strategy consists of: i) a reference generator that calculates the target yaw rate in real time based on the current values of steering wheel angle and vehicle velocity, in order to follow a desired optimal understeer characteristic; ii) a high-level controller which generates the overall traction/braking force and yaw moment demands based on the accelerator/brake pedal and on the error between the target yaw rate and the actual yaw rate; iii) a control allocator which outputs the reference torques for the individual wheels. A driver model was implemented to work out the brake/accelerator pedal inputs and steering wheel angle input needed to follow a generic trajectory. In a first implementation of the model, a circular trajectory was adopted, consistently with the "skid-pad" test of the FSAE competition. Results are promising as the vehicle with TVC achieves up to � 9% laptime savings with respect to the vehicle without TVC, which is deemed significant and potentially crucial in the context of the FSAE competition.

Item Type: Book Section
Additional Information: Series ISSN: 2195-4356
Page Range: 1075-1083
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 09 Oct 2019 15:51
Last Modified: 19 Jun 2020 15:47
URI: http://shura.shu.ac.uk/id/eprint/24918

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