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Torque Vectoring Controls for Multi-architecture Battery Electric Vehicles

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Torque Vectoring Controls for Multi-architecture Battery Electric Vehicles.

Rel. Andrea Tonoli, Angelo Bonfitto. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2022

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This study investigates a method for enhancing Battery Electric Vehicle (BEV) longitudinal and lateral stability in a broad range of conditions. As with the state-of-the-art, a hierarchical control is developed: the upper layer uses vehicle state errors and predicted yaw acceleration in order to generate a corrective yaw moment using a sliding mode control (SMC); the lower layer distributes the total requested torque to the electric motors using a vertical-load-based torque distribution algorithm. The main contribution of this work is the additional term inside the yaw moment controller: the expected yaw acceleration of the vehicle is predicted, allowing a more effective action of the controller and thus reducing path errors. Another contribution of this work is the development of a sliding mode-based cruise control, featuring good responsiveness when the velocity error is high and robustness when it approaches zero. The proposed control system is verified in the MATLAB-Simulink environment, using a seven degree of freedom vehicle model (longitudinal, lateral, yaw and rotation of each wheel) and the bicycle model as reference model. Several simulations under different driving scenarios are run and the results are discussed, highlighting the merit of the proposed control strategy. Furthermore, the simulation environment that has been built allows the benchmarking of different BEV architectures. When changing the number and position of the electric motors, the developed integrated vehicle stability controller is able to split the required torque among the available motors in order to optimize available traction depending on the vehicle configuration.

Relators: Andrea Tonoli, Angelo Bonfitto
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 186
Corso di laurea: Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo)
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: University of Windsor
URI: http://webthesis.biblio.polito.it/id/eprint/24346
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