A Safety Requirements’ Adaptive NMPC Strategy for Electric Vehicle Stability Control With Computationally Efficient Optimization

Handling and stability are vital for vehicle safety, especially under extreme conditions, such as low friction surfaces, violent steering, and urgent acceleration/deceleration. Electric vehicles (EVs) are a promising way to improve the stability due to their rapid and accurate responses. However, vehicle states are influenced by highly coupled and nonlinear dynamics. The safety requirements are different under various conditions. To solve the above problems, a nonlinear model predictive control (NMPC)-based strategy is proposed for stability control. First, a 3-D stability space, which considers yaw, lateral, and longitudinal motions, is proposed to analyze the degree of vehicle stability under different conditions. Then, an adaptive control strategy is proposed to meet various safety requirements. Moreover, to improve the control performance under extreme conditions, a nonlinear vehicle dynamic model is adopted to predict the future states. To enable vehicular applications with low-cost hardware, a Pontryagins minimum principle (PMP)-based solving method is proposed for computationally efficient online optimization. Finally, hardware-in-loop experiments are conducted to check the effectiveness and superiority of the proposed method. The experimental results show that the proposed strategy has a better performance improving vehicle handling and stability under extreme conditions.