Pre-Stability Control for In-Wheel-Motor-Driven Electric Vehicles With Dynamic State Prediction
Miao-Zhuang Tian, Qixiang Zhang, Duanyang Tian, Liqiang Jin, Jianhua Li, Feng Xiao
Abstract
In-wheel-motor-driven electric vehicles (IWM-EVs) provide more potential to enhance vehicle stability performance. However, traditional stability control relies on the current status fed back by sensors for stability judgment and control, only taking effect after the vehicle has already become unstable. In response to this issue, this paper proposes a pre-stability control strategy based on a hybrid dynamic state prediction method to predict dangerous driving conditions and intervene in vehicle stability control in advance. First, a driver-vehicle model is established to characterize the driver's driving intention and obtain the vehicle's ideal motion responses. Then, the methodology for implementing vehicle pre-stability control is introduced, which mainly includes sideslip angle estimation utilizing the extended Kalman filter, a hybrid dynamic state prediction approach based on vehicle model and data trends, and a vehicle pre-stability judgment method. Subsequently, a vehicle hierarchical controller is designed to achieve pre-stability control. The upper-level controller focuses on calculating the required additional yaw moment, and the lower-level controller aims to optimize torque distribution among the four wheels. Finally, the proposed pre-stability control strategy is validated by the hardware-in-the-loop test bench. The results show that the proposed control strategy can intervene in dangerous driving conditions in advance, and its mean errors of the yaw rate and sideslip angle are reduced by over 17.1% and 23.5%, respectively, compared with the traditional method, which significantly enhances vehicle stability and driving safety.