Kang, Qiao (Tongji University), Meng, Dele (Tongji University), Jiang, Yuyao (Shanghai Customs College), Zhao, Chunlai (Dongfeng Motor Corporation), Chu, Hongqing (Tongji university), Gao, Bingzhao (Tongji University)

Torque Vectoring Control of Distributed Drive Electric Vehicles Using Fast Iterative Model Predictive Control

Scheduled for presentation during the Invited session "Control, Simulation and Modeling of Intelligent Vehicles" (ThA3), Thursday, October 31, 2024, 10:30−10:50, Room T3

7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, Oct 30 - Nov 1, 2024, Dalian, China

This information is tentative and subject to change. Compiled on July 9, 2025

Abstract

Aiming at the torque vectoring control of distributed drive electric vehicles under challenging condition, this article introduces a Fast Iterative MPC (FI-MPC) framework with rule-based iteration criteria to handle the trade-off between control performance and solving time. The core concept of FI-MPC involves incorporating the unmodeled nonlinear dynamics of the vehicle or tires into a linear framework to achieve optimal iterative control solutions. Co-simulation results confirm that FI-MPC improves yaw rate tracking performance, restraint of sideslip angle and other indexes in nonlinear zone, while does not significantly increase solving time compared with linear MPC, demonstrating its application potential.