Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks

Area of Science:

• Automotive Engineering
• Control Systems
• Vehicle Dynamics

Background:

• Heavy-duty electric trucks with distributed drive systems face challenges in maintaining yaw stability.
• Existing control strategies often struggle to balance stability enhancement with optimal tire utilization.

Purpose of the Study:

• To develop and validate a layered control strategy for improving yaw stability in distributed drive electric trucks.
• To optimize the yaw moment controller using a hybrid optimization algorithm and enhance torque distribution for better vehicle control.

Main Methods:

• A layered control strategy was designed, with an upper layer using a Linear Quadratic Regulator (LQR) optimized by a Genetic Algorithm and Particle Swarm Optimization (GA-PSO).
• The lower layer employed Quadratic Programming for optimal torque distribution, minimizing tire utilization.
• Simulations were conducted on Matlab/Simulink Trucksim under serpentine and double lane change conditions, comparing the proposed strategy against SMC, LQR, and no control.

Main Results:

• The proposed strategy reduced average yaw rate by up to 42.15% and average sideslip angle by up to 52.3% compared to other methods.
• Optimized torque allocation decreased average tire utilization by 42.6%, enhancing the stability control margin.
• Significant improvements in driving stability were demonstrated under various working conditions.

Conclusions:

• The developed layered control strategy effectively enhances yaw stability in distributed drive electric trucks.
• The hybrid optimization and optimized torque distribution contribute to improved vehicle safety and efficiency.
• The findings validate the proposed approach for practical application in electric heavy-duty vehicles.