Stability control of in-wheel motor driven vehicle based on extension pattern recognition

Area of Science:

• Automotive Engineering
• Control Systems
• Robotics

Background:

• In-wheel motor electric vehicles offer independent wheel torque control, enabling advanced stability management.
• Existing control strategies may not fully exploit the potential of distributed electric propulsion for dynamic stability.

Purpose of the Study:

• To propose and validate a novel stability control strategy for in-wheel motor electric vehicles.
• To enhance vehicle dynamics and safety using an extension pattern recognition method.
• To minimize energy consumption through optimal torque distribution.

Main Methods:

• Vehicle dynamic modeling using Matlab/Simulink and Carsim.
• Development of a two-degree-of-freedom (2-DOF) vehicle reference model.
• Implementation of an extension pattern recognition algorithm to classify stability states.
• Design of fuzzy controllers for yaw rate and sideslip angle control.
• Optimization of motor torque distribution to minimize total energy loss.

Main Results:

• A four-pattern stability control system (no control, yaw rate control, yaw rate and sideslip angle joint control, sideslip angle control) was established.
• Fuzzy controllers effectively generated additional yaw moment for stability.
• Optimal torque distribution significantly reduced total energy loss across the four motors.
• Simulations and Hardware-in-the-Loop (HIL) tests confirmed the strategy's feasibility and effectiveness.

Conclusions:

• The proposed extension pattern recognition-based stability control strategy is effective for in-wheel motor electric vehicles.
• The integrated approach enhances vehicle stability, optimizes energy efficiency, and ensures safety.
• This method provides a robust framework for advanced vehicle dynamics control.