Energy-Optimized Longitudinal–Steering Coordinated Torque Vectoring for an In-Wheel-Motor-Driven Electric Vehicle

Four-wheel-drive electric vehicles equipped with independently controllable driving, braking, and steering actuators provide additional degrees of freedom for reducing electric-machine and tire-loss energy. This paper presents a real-time longitudinal–steering coordinated torque-vectoring framework for a vehicle driven by four in-wheel motors. A model-predictive active-front-steering controller coordinates the front-wheel steering angle and external yaw moment to reduce steering resistance and lateral tire-slip loss. A reduced inter-axle propulsion problem is analyzed using the Karush–Kuhn–Tucker conditions, and its speed-dependent switching threshold is calibrated offline by particle swarm optimization. Regenerative braking is allocated by an Energy-Optimized Distribution curve subject to ideal-distribution and regulatory constraints. For general positive-torque operation, sequential quadratic programming distributes the four wheel torques by considering motor input power, tire-slip energy, total torque, yaw-moment demand, and actuator limits. Hardware-in-the-loop results under the United States high-acceleration driving cycle and a double-lane-change maneuver show that the proposed strategy reduces energy consumption relative to uniform and tire-utilization-based torque-vectoring strategies.