Automotive Science and Engineering

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In this paper, a multi-level hierarchical control method for enhancing electric vehicle (EV) stability with four independent in-wheel motors is proposed. In the high-level motion controller, a sliding mode controller is used to calculate the total desired force and yaw moment, and in the low-level control allocation, an optimal energy-efficient control allocation scheme is presented to provide optimally distributed torques for four in-wheel motors. Moreover, both handling performance and energy savings are investigated in this research and evaluated via a co-simulation approach using MATLAB/Simulink, and CarSim. With a torque distribution algorithm based on energy efficiency optimization, the EV is controlled with and without a controller in J-turn and lane change maneuvers. The simulation results show that the proposed torque control system and torque distribution algorithm can maintain stability, reduce energy consumption, and track the desired values of yaw rate and longitudinal velocity of the vehicle in the mentioned maneuvers.

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Path-tracking for autonomous vehicles at physical handling limits is severely challenged by nonlinear tire saturation, which degrades conventional Active Front Steering (AFS) systems. This study proposes a hierarchical control architecture coordinating AFS, Torque Vectoring Control (TVC), and Active Rear Camber (ARC) to enhance path-tracking accuracy under limit driving conditions. An upper-level Linear Model Predictive Control (LMPC) algorithm is designed to calculate the virtual corrective yaw moment and the optimal rear camber angle. Simultaneously, a lower-level three-mode Quadratic Programming (QP) framework dynamically allocates torques based on instantaneous tire friction capacities. MATLAB/CarSim co-simulations of severe Double Lane Change (DLC) maneuvers validate the system's efficacy. Quantitatively, during a 140 km/h maneuver on dry asphalt, the proposed fully integrated system expands the maximum achievable lateral acceleration to 0.8g. Compared to the baseline AFS configuration, it significantly reduces the root-mean-square (RMS) and peak lateral tracking errors by 32% (to 0.239 m) and 27% (to 0.687 m), respectively, while concurrently decreasing the peak steering demand by 27%. Furthermore, under low-friction critical conditions (60 km/h, μ=0.5), the controller effectively limits sideslip oscillations and prevents vehicle spin-out. Ultimately, the formulated hierarchical framework manages the over-actuation dynamically, yielding a peak execution time that consumes only 74% of the real-time step limit, providing a highly viable and computationally efficient strategy for automotive Electronic Control Unit (ECU) implementation.