Real-time model predictive control of connected electric vehicles

ABSTRACT

Electric Vehicles (EVs) motors develop high torque at low speeds, resulting in a high rate of acceleration with the added advantage of being fitted with smaller gearboxes. However, a rapid rise of torque in EVs fitted with central drive powertrains can create undesired torsional oscillations, which are influenced by wheel slip and flexibility in the halfshaft. These torsional oscillations in the halfshaft lead to longitudinal oscillations in the vehicle, thus creating problems with regard to comfort and drivability. The significance of using wheel slip in addition to halfshaft torsion for design of anti-jerk controllers for EVs has already been highlighted in our previous research. In this research, we have designed a look-ahead model predictive controller (LA-MPC) that calculates the required motor torque demand to meet the dual objectives of increased traction and anti-jerk control. The designed LA-MPC will improve drivability and energy consumption in connected EVs. The real-time capability of the LA-MPC has been demonstrated through hardware-in-the-loop experiments. The performance of the LA-MPC has been compared to other controllers presented in the literature. A validated high-fidelity longitudinal-dynamics model of the Rav4EV, which is the test vehicle of our research has been used to evaluate the controller.     

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