On low-friction surfaces, vehicle stability is significantly influenced by nonlinear vehicle dynamics. To realize stability improvement, a real-time nonlinear model predictive control (NMPC) strategy that combines torque vectoring control (TVC) and rear-wheel steering (RWS) is proposed. A multitarget optimization objective function that considers system state tracking, stability, control constraints, and actuation energy consumption is established. Then, a fast-solving algorithm based on Pontryagin’s minimum principle (PMP) is proposed to reduce the heavy computational burden of NMPC and satisfy the real-time computation requirement of vehicular applications. The effectiveness of the proposed fast-solving algorithm is verified by comparing its closed-loop performance with interior point optimization (IPOPT). A series of hardware-in-the-loop (HIL) experiments with the real human driver is conducted to verify the effectiveness of the proposed fast-solving algorithm and combined control strategy. Furthermore, the HIL results show that the proposed combined control strategy can improve a vehicle’s handling stability on a low-friction surface and has a better performance and lower actuation energy consumption than the TVC-only method.


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    Title :

    A Real-Time NMPC Strategy for Electric Vehicle Stability Improvement Combining Torque Vectoring With Rear-Wheel Steering


    Contributors:
    Liu, Hanghang (author) / Zhang, Lin (author) / Wang, Ping (author) / Chen, Hong (author)


    Publication date :

    2022-09-01


    Size :

    3789903 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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