Rollover Prevention Control of Electric Vehicles Based on Multi-Objective Optimization Coordination Under Extreme Conditions
Ping Wang, Xiyue Zhang, Jianpeng Shi, Bin Gou, Lin Zhang, Hong Chen, Yunfeng Hu
Abstract
When a vehicle is driving at a high speed on a high-adhesion road, the improper operation could greatly increase the risk of roll or even rollover. In this paper, an MPC-based control strategy is proposed for a four-wheel independent motor-drive electric vehicle to prevent the vehicle from rolling over under extreme driving conditions. First, considering tire force saturation, the nonlinear vehicle dynamic model is applied to predict lateral, yaw and roll motion under extreme conditions. Second, based on the real-time rollover index changing with the vehicle states, the control region is divided into stable, critically stable, unstable regions with different control requirements. Then, according to the driver behaviors and the collected road conditions, the identified stability boundaries are utilized as state constraints. By changing the weighting factors and constraint values of the model predictive controller, multiple safety requirements for different regions are dynamically coordinated to improve vehicle overall performance. Finally, CarSim and MATLAB/Simulink co-simulation, and hardware-in-the-loop simulation test results show that the vehicles handling performance, stability, and rollover prevention performance are effectively improved under high speed turning.