Research on Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicle Based on State Estimation

Area of Science:

• Automotive Engineering
• Control Systems
• Robotics

Background:

• Electric vehicles (EVs) with four-wheel independent drive (FWID) present unique challenges in state estimation and lateral stability control.
• Accurate estimation of vehicle states like tire forces and velocities is crucial for effective control.
• Maintaining lateral stability is paramount for vehicle safety, particularly under varying road conditions.

Purpose of the Study:

• To propose a hierarchical framework for integrated vehicle state estimation and lateral stability control in FWID electric vehicles.
• To enhance the accuracy and efficiency of vehicle state estimation.
• To improve the handling and stability performance of EVs.

Main Methods:

• A hierarchical estimation method using Kalman Filter (KF) and Extended Kalman Filter (EKF) to estimate wheel vertical load, lateral forces, and velocities.
• A hierarchical lateral stability control system with upper and lower control layers for torque distribution.
• Integration of a three-degrees-of-freedom vehicle model with the Magic Formula Tire (MF-T) model.

Main Results:

• The hierarchical estimation method demonstrated high computational efficiency and superior accuracy in estimating vehicle states.
• The hierarchical control system significantly improved vehicle handling and lateral stability, particularly under low and medium road adhesion.
• Simulations and Double Lane Change (DLC) tests validated the effectiveness of the proposed framework.

Conclusions:

• The developed hierarchical framework provides an effective solution for state estimation and lateral stability control in FWID EVs.
• The approach offers significant improvements in vehicle performance and safety across diverse road conditions.
• This work contributes to the advancement of autonomous and electric vehicle control technologies.