Feedforward control approach for all-wheel drive super cars with torque vectoring capabilities
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hdl:2117/334189
Document typeMaster thesis
Date2020-07-09
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Abstract
To improve their performances, modern sport cars can use Torque Vectoring managed by a control
allocation system to generate a desired yaw moment. The goal here is to fit a reference yaw moment
calculated through a model and find the torques to apply on each wheel to achieve it. However, drivers
will not accept a synthetic driving feeling that can occurs with a feedback control. This is why this
project investigates a model-based and feed-forward controller that enables to manage the whole
vehicle dynamics, to reach a desired behaviour. The work presented here is based on a control TV
management strategy first developed for Porsche. It aims at adapting this strategy to a hybrid AWD
vehicle with the rear axle powered by an ICE and two electric motors powering the front wheels. A
double track vehicle model is used to emulate the car’s dynamics, associated to a Pacejka’s Tyre model.
Starting with the algorithm’s architecture, all the equations behind each step have been found.
Therefore, a generical algorithm has been created to identify the process and the requirements for a
future implementation in a Model-In-the-Loop developed by Mclaren. An optimizing function has been
created to find the optimal longitudinal distribution while the whole process adjusts the TV moment
to reach a reference yaw rate. Then, the complete algorithm has been implemented on
Matlab/Simulink as a first try. However, a misunderstanding of one of the algorithm’s step has not
enabled to obtain satisfying results.
DegreeMÀSTER UNIVERSITARI EN ENGINYERIA D'AUTOMOCIÓ (Pla 2012)
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