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This paper proposes a novel allocation-based control method for four-wheel independently actuated electric vehicles. In the proposed method, both actuator dynamics and input/output constraints are fully taken into consideration in the control design. First, the actuators are modeled as first-order dynamic systems with delay. Then, the control allocation is formulated as an optimization problem, with the primary objective of minimizing errors between the actual and desired control outputs. Other objectives include minimizing the power consumption and the slew rate of the actuator outputs. As a result, this leads to frequency-dependent allocation that reflects the bandwidth of each actuator. To solve the optimization problem, an efficient numerical algorithm is employed. Finally the proposed control allocation method is implemented to control a four-wheel independently actuated electric vehicle.

In this paper, the problem of stability control of an electric vehicle is addressed. To this aim, it is required that the vehicle follows a desired yaw rate at all driving/road conditions. The desired yaw rate is calculated based on steering angle, vehicle speed, vehicle geometric properties as well as road conditions. The vehicle response is modified by torque vectoring on front and/or rear axles. This control problem is subject to several constraints. The electric motors can only deliver a certain amount of torque at a given rotational speed. In addition, the tire capacity also plays an important role. It limits the amount of torque they can transfer without causing wheel to slip excessively. These constraints make the Model Predictive Control (MPC) approach a suitable choice, because it can explicitly consider the constraints of the control problem, in particular the tire capacity constraint, and help prevent tire saturation, which is often the cause of vehicle instability.

Steering torque feedback, or steering feel, is widely regarded as an important aspect of driver interface to road feel. To generate a steering feel with the appropriate level of fidelity required by a driver-vehicle system or a driving simulator, it is essential to gain a good understanding of various important influencing factors of steering torque feedback. This paper presents a comprehensive study and analysis of internal and external factors that strongly affect steering torque feedback. A steering torque feedback model with sufficient fidelity is established and verified as the base for this study. The individual- and collective-level influences of these factors on steering torque feedback are analyzed in both time domain and frequency domain, with guidelines provided on how to properly use these influencing factors to control their negative effects in modeling steering torque feedback.