Torque-Vectoring Control for an Autonomous and Driverless Electric Racing Vehicle with Multiple Motors 2017-01-1597
Electric vehicles with multiple motors permit continuous direct yaw moment control, also called torque-vectoring. This allows to significantly enhance the cornering response, e.g., by extending the linear region of the vehicle understeer characteristic, and by increasing the maximum achievable lateral acceleration. These benefits are well documented for human-driven cars, yet limited information is available for autonomous/driverless vehicles. In particular, over the last few years, steering controllers for automated driving at the cornering limit have considerably advanced, but it is unclear how these controllers should be integrated alongside a torque-vectoring system. This contribution discusses the integration of torque-vectoring control and automated driving, including the design and implementation of the torque-vectoring controller of an autonomous electric vehicle for a novel racing competition. The paper presents the main vehicle characteristics and control architecture. A quasi-static model is introduced to predict the understeer characteristics at different longitudinal accelerations. The model is coupled with an off-line optimization for the a-priori investigation of the potential benefits of torque-vectoring. The systematic computation of the achievable cornering limits is used to specify and design realistic maps of the reference yaw rate, and a non-linear feedforward yaw moment contribution providing the reference cornering response in quasi-static conditions. A gain scheduled proportional integral controller increases yaw damping, thus enhancing the transient response. Simulation results demonstrate the effectiveness of the proposed approach.
Citation: Chatzikomis, C., Sorniotti, A., Gruber, P., Bastin, M. et al., "Torque-Vectoring Control for an Autonomous and Driverless Electric Racing Vehicle with Multiple Motors," SAE Int. J. Veh. Dyn., Stab., and NVH 1(2):338-351, 2017, https://doi.org/10.4271/2017-01-1597. Download Citation
Christoforos Chatzikomis, Aldo Sorniotti, Patrick Gruber, Matthew Bastin, Raja Mazuir Shah, Yuri Orlov
University of Surrey, Charge Automotive Ltd.
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