The wheel hub motor–driven electric vehicle, characterized by its independently
controllable wheels, exhibits high torque output at low speeds and superior
dynamic response performance, enabling in-place steering capabilities. This
study focuses on the control mechanism and dynamic model of the wheel hub motor
vehicle’s in-place steering. By employing differential torque control, it
generates the yaw moment needed to overcome steering resistance and produce yaw
motion around the steering center.
First, the dynamic model for in-place steering is established, exploring the
various stages of tire motion and the steering process, including the start-up,
elastic deformation, lateral slip, and steady-state yaw. In terms of control
strategy, an adaptive in-place steering control method is designed, utilizing a
BP neural network combined with a PID control algorithm to track the desired yaw
rate. Additionally, a control strategy based on tire/road adhesion ellipse
theory is developed to enhance vehicle handling stability under different road
conditions.
The simulation results indicate that the control strategy effectively optimizes
the vehicle’s steering response, reducing the center of gravity displacement by
approximately 50% and 75% along the y-axis and x-axis, respectively, under
high-friction conditions, while maintaining the maximum tracking error for the
desired yaw rate at around 0.5%. Under low-friction conditions, the center of
gravity displacement along the y-axis decreases from a maximum of 0.32 m to 0.19
m, with the tracking error for the desired yaw rate stabilizing at approximately
0.6%. This ensures the vehicle’s stability and safety during extreme steering
maneuvers. This research provides a theoretical foundation and practical
reference for the design of control systems in future distributed drive electric
vehicles.