Torque Vectoring for High-Performance Electric Vehicles: An Efficient MPC Calibration
Alberto Lucchini, Simone Formentin, Matteo Corno, Dario Piga, Sergio M. Savaresi
Abstract
In electric vehicles with in-wheel motors, torque vectoring is an effective technology to reach high-level performance in sport driving conditions. To vary the torque at each wheel while guaranteeing proper safety constraints, Model Predictive Control (MPC) has been shown to be the most suited strategy. However, MPC requires simple models and significant tuning effort to be really effective. In this letter, we propose a simple predictive grey-box model of the vehicle dynamics and an efficient calibration strategy to tune the MPC cost with little additional experimental effort. Results on a full-fledged multi-body simulator complete this letter.
Topics & Concepts
TorqueModel predictive controlComputer scienceCalibrationSimple (philosophy)Control theory (sociology)Electric vehicleAutomotive engineeringEngineeringControl (management)Artificial intelligencePower (physics)PhysicsEpistemologyPhilosophyQuantum mechanicsThermodynamicsVehicle Dynamics and Control SystemsReal-time simulation and control systemsElectric and Hybrid Vehicle Technologies