Research On Direct Yaw Moment Control For Distributed Drive Electric Vehicles

In recent years, under the dual pressures of energy and environmental protection, energy conservation and environmental performance of vehicles are gradually taken more attention and electric vehicles(EVs) naturally become the focus of current research. Distributed drive EVs are highlighted by national research scholars due to the unique structural features and performance advantages. Meanwhile, active safety control developed in recent years becomes a hot topic in this direction. Distributed drive EV replaces internal combustion engine and drivetrain with battery and hub motors embedded in the wheel, so that it has advantages of smaller volume, larger power and higher transmission efficiency. The realization of each wheel driving/braking force independent and controllable makes it necessary to research on distributed drive EVs’ stability control problems. This paper aims to research on yaw stability control and torque distribution problem based on the characteristics of the distributed drive EVs.View that current simulation model is simple, parametric modeling software Carsim is selected to build distributed drive EV dynamic simulation model. It provides distributed drive EV interface via drive system configuration. Because Carsim does not provide motor model, brushless DC motor model is built in Matlab/Simulink. Finally, Carsim/Simulink model co-simulation will be integrated into a complete distributed drive EV model.The accuracy of the vehicle status is very important to the vehicle yaw stability control, so this part develops Extended Kalman Filter(EKF) vehicle state observer to estimate vehicle key kinetic parameters: longitudinal speed, vehicle yaw rate and vehicle sideslip angle. The established simulation model tests in the double lane condition and snake condition. The results show that the EKF observer can track the vehicle status well even in variable speed condition.Then, the paper analyzes the vehicle’s tire important influence on the stability and pointes out the relationship between vehicle yaw rate, sideslip angle and vehicle stability. The additional yaw moment is calculated using the method of feed forward and feedback control to follow desired vehicle yaw rate. And last, simulation is done to verify the reliability.Finally, optimizing allocation of yaw moment is studied on the basis of the obtained additional yaw moment. We select tire road adhesion utilization as optimization target and find a minimum within the constraints of a plurality of boundary conditions. Distribute drive EV stability control is achieved when each wheel motor drive torque is given. The simulation results show that the method can improve vehicle driving performance in extreme conditions.