Research On Optimal Torque Allocation Strategy For Distributed Drive Electric Vehicle

As an important means of transportation in the future society,new energy vehicles driven by pure motors have received widespread social attention.Among them,the distributed drive electric vehicle is considered as one of the main vehicles in the future low-carbon society and smart city because of its compact structure,high transmission efficiency and easy control.It is also an important development direction and academic research hotspot of new energy vehicles.How to take advantage of the independent controllable torque of each wheel of distributed drive electric vehicle,optimize the distribution of torque between wheels,improve vehicle safety and stability,optimize vehicle energy consumption,achieve good stability and economic control,is a difficult problem for the allocation of torque to distributed drive electric vehicle,and it is also the research focus of this paper.Firstly,the whole vehicle torque distribution system with hierarchical control structure is designed.The upper layer of the system is the motion tracking layer.The direct yaw moment control strategy based on the neural network PID control algorithm is adopted.By comparing the deviation between the actual sideslip angle and yaw angular velocity of the vehicle and the steady-state response parameters of the ideal vehicle model,the momentum optimization term is introduced to update the weights of the neural network on-line,and the direct yaw moment needed to track the ideal parameters is determined and Calculate the generalized control force for vehicle operation.The lower layer of the system is the torque distribution layer.The torque distribution strategy is based on the generalized control force demand.Based on the particle swarm optimization algorithm,the optimal driving efficiency and the lowest tire load rate are optimized.The distribution coefficient is assigned to each wheel torque by the torque distribution coefficient.Then,based on the CarSim/Simulink distributed drive electric vehicle dynamics control simulation platform,step-steering and single-lane-shifting experiments on ice and snow pavement at medium speed and double-lane-change experiments on wet and slippery pavement at high speed are carried out.The results show that the designed neural network PID direct yaw moment control strategy can optimize the parameters of the PID control in real time,effectively determine the direct yaw moment,thus ensuring the vehicle's handling stability.Conducting straight-line driving experiments and mediumspeed steady-state steering conditions experiments in NEDC working conditions and China light-duty vehicle test cycle for passenger car,The results show that the optimal allocation strategy of lower-level torque can reduce the energy consumption under straight-line driving conditions,and ensure the stability and economy under steering driving conditions.The validity and feasibility of the hierarchical control strategy are verified by the working condition experiment.Finally,based on the dSPACE system,the CarSim/dSPACE hardware-in-the-loop simulation experiment platform was built.In the experimental situation similar to the actual vehicle operating environment,the vehicle torque distribution strategy was verified by double-lane-change condition.The results show that the control system designed in this paper achieves the preset functions,and the proposed control algorithm can ensure vehicle handling stability and save vehicle energy consumption.