Stability (learning theory)

Physics

Torque

Automotive engineering

Torque steering

Machine learning

Engineering

Artificial intelligence

Power (physics)

Control (management)

Electric vehicle

Steering wheel

Computer science

Quantum mechanics

Control theory (sociology)

Thermodynamics

IEEE Transactions on Transportation Electrification

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.

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