Integrated control method for path tracking and lateral stability of distributed drive electric vehicles with extended Kalman filter–based tire cornering stiffness estimation

Abstract

Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)–based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control–based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions.

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

“…Under different road conditions, the developed state observers are able to estimate the lateral deflection angle of the vehicle very well with very small estimation errors, which can provide reliable vehicle state information for vehicle stability management. Qi et al [23] proposed a novel electronic stability control system for electric vehicles based on Kalman filtering for lateral deflection angle estimation, which achieves accurate lateral deflection angle estimation and improves the stability control system by using the combined model error and external disturbances as an extended Kalman filtering algorithm.…”