Electronic differential control based on speed and optimum slip ratio estimation for all-electric vehicles with in-wheel motors

Vehicle 2-degree of freedom (DOF) kinematic and dynamic models are derived. The former, which uses fixed parameters, is often used for speed-based electronic differential control, but this method does not yield accurate results under varying running situations. In contrast, the latter, which depends on the tire adhesion limit to produce tire saturation force, is typically adopted for torque-based electronic differential control. However, this method also faces many difficulties in real-time implementation, and its theoretical maturity is not strong. To combine the advantages of speed-based electronic differential control and torque-based electronic differential control, this paper focuses on speed and optimum slip ratio as key factors. Additionally, to address the difficulties associated with nonlinear modeling by leveraging the simplicity of linear modeling in design and implementation, this study presents an electronic differential control based on speed and optimum slip ratio estimation for all-electric vehicles with in-wheel motors. It aims to maintain the maneuvering ability of the driver at the maximum adhesion limit. Even when the two driving wheels are subjected to uneven external disturbances from the road surface, they maintain a synchronous speed when driving on a straight line or a differential speed when turning. Simulation validation confirms that the proposed method enhances safety for in-wheel motor electric vehicles in urban scenarios involving adverse weather conditions (e.g., rain, snow, ice) and aggressive lane-changing maneuvers. Experimental validation confirms the static performance of the motor controller and the differential control capability of the two drive wheels. These findings lay a foundation for improving extreme-condition adaptability through three future directions: adaptive tire dynamics integration, hierarchical energy-stability control architectures, and real-time deployment validated via hardware-in-the-loop testing.