Due to high customer demand for safety degree in automotive, the safety functions in vehicles have a special meaning. Torque vectoring function as an active safety function, generates an additional yaw moment by means of asymmetrical driving forces on both vehicle sides, in order to improve or correct the steering behaviour. In comparison to the conventional vehicles, the electric vehicles have the advantage to realize this function without special differential gearbox. Furthermore, a high-dynamic torque can be performed by an electrical drive train. The entire torque vectoring function in electric vehicles includes the control of the electrical drive train, the mechanical drive train and the vehicle dynamic. In order to implement a high-performance application, the challenge will be faced, that both the system constraints and uncertainties as well as the control dynamic should be taken into account. In order to achieve a satisfying compromise, the model predictive control (MPC) is chosen as theoretical fundamental for this work, surveyed and implemented for the application. The critical control problems in electrical drive train are represented by parameter variation of the induction motor, the dead time, as well as the current and voltage limitations. Two solutions are implemented to handle the parameter variation. In the first one, the min-max control method is applied. The control problem is described by a linear parameter-varying (LPV) system with polytopic uncertainties. The robustness of the system is ensured by solving the worst case restricted by a Lyapunov function. The other solution is based on the so-called tube MPC method. Instead of a LPV system, a linear time invariant (LTI) system is used. The deviation between the LTI and the real system are limited by a robust positive invariant (RPI) set. By determining the minimum RPI (mRPI), both the robustness and the optimality are ensured. In contrast to other solutions, the system constraints are reformulated by torque constraint in this work instead of the current and voltage constraints. The advantage is that, no approximation to fulfill the constraint form is necessary and the optimality is therefore enhanced. In the mechanical drive train, the primary task is actively damping the oscillation of the transmitted torque. By means of a MPC controller with feedback compensation, the torsional oscillation on the side shaft is significantly suppressed. To complete the torque vectoring function, the yaw rate control as the main component and the tire slip control as auxiliary component are implemented. In addition, the operation strategy is implemented to provide a reliable reference yaw rate depending on the driving situations. Since only standard sensors are available in vehicles, a strategy is implemented for the control software to estimate the values, which are not measurable. The aforementioned subsystems are integrated and validated on a Hardware-in-the-Loop (HiL) test bench. The driving maneuvers specified in ISO 7401 are applied for the tests. The results show, that the implemented function is able to both improve the steering behavior of the vehicle system, and guarantee the vehicle stability up to the system constraints.


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    Titel :

    Optimization-based robust control for high-performance torque vectoring in electric vehicles operated by induction traction motors


    Weitere Titelangaben:

    Optimierungsbasierte robuste Regelung für leistungsstarkes Torque-Vectoring in elektrischen Fahrzeugen mit Asynchronmotoren


    Beteiligte:
    Hu, Zheng (Autor:in) / Hameyer, Kay (Akademische:r Betreuer:in) / Mertens, Axel (Akademische:r Betreuer:in)

    Erscheinungsdatum :

    2017-01-01


    Format / Umfang :

    224 Seiten : pages


    Anmerkungen:

    RWTH Aachen University, Diss., 2017; Aachener Schriftenreihe zur Elektromagnetischen Energieumwandlung 24, 224 Seiten :(2017). = RWTH Aachen University, Diss., 2017



    Medientyp :

    Sonstige


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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