A Nash-Equilibrium-Based AFS-TVA Coordinated Control System for Distributed Drive Electric Vehicles Considering Safety and Energy
Zhenwu Fang, Dongming Han, Jinxiang Wang, Wenpeng Wei, Dawei Pi, Guodong Yin
Abstract
Active front steering (AFS) and torque vector allocation (TVA) have been regarded as two independent agents of distributed drive electric vehicles (DDEVs), which are used to improve vehicle maneuverability and stability. However, the difference in control objectives may generate potential objective conflicts between AFS and TVA, which results in increased energy loss and lower vehicle safety in electric vehicles. To this end, a hierarchical Nash-equilibrium AFS-TVA game framework is proposed to address the problem of goal inconsistency between path tracking, vehicle stability, and energy saving. First, the Takagi-Sugeno (T-S) fuzzy method is employed to address the model mismatch caused by the uncertainties of vehicle speed and driver characteristics. And the dynamic interactive model between the agents is constructed through the noncooperative model predictive control (MPC) method. Then, Nash open-loop game strategy for the AFS and TVA is proposed to synergize the path-tracking performance and vehicle stability. Furthermore, the in-wheel motor control execution layer adopts the particle swarm optimization algorithm to allocate the driving torque in real time to ensure optimal energy consumption. Finally, the hardware-in-the-loop (HIL) experiments are conducted to validate the proposed coordinated control method. The results show that the proposed Nash-equilibrium game strategy is effective to enhance vehicle safety and reduce energy consumption.