Research On Adaptive Control Of Driving Force Of Four-wheel-driven Electric Cargo Vehicles

Wheel-driven electric vehicles with on-board motors are the key development direction in the field of electric vehicles,domestic and foreign scholars have carried out a lot of research work on this topic.Most of the existing researches focus on passenger vehicles,but the characteristics of the wheel-motor-driven technology and its successful application in the massly produced electric vehicles indicate that the wheel-motor-driven technology is more suitable for large commercial vehicles such as buses or cargo vehicles.In this thesis,the adaptive control of driving force of four-wheel-driven electric cargo vehicles with on-board motors is studied based on the characteristics of large load-variation range and the actual load is difficult to measure in cargo vehicles.The main work carried out in this thesis is as follows:(1)Vehicle dynamic system modeling.Build a vehicle model which is composed of vehicle body model,driver model,electromotor model,steering system model,wheel model and tire model,and use the commercial vehicle simulation software Truck Sim to validate this model.(2)Research on direct yaw-moment control algorithm based on sliding mode control theory.The influence of the vehicle yaw rate and the vehicle sideslip angle on vehicle lateral stability is analyzed based on the two-degree-of-freedom vehicle model and tire model,and then the yaw rate constrained by the sideslip angle is proposed as the controlled variable of the controller.Based on sliding mode control theory,a direct yaw-moment control algorithm is designed and analyzed with simulation.(3)Research on adaptive control algorithm of direct yaw-moment based on RBF neural network sliding mode control.Aiming at the problem that the vehicle load affects the accuracy and stability of the sliding mode controller,the RBF neural network sliding mode controller,which is composed of the RBF neural network controller,the RBF neural network yawing motion characteristics identifier and the sliding-mode variable-structure component,is designed to achieve adaptive control in different vehicle load.The RBF neural network is trained offline with the method of the orthogonal least square method,and it also undergoes an online correction with the gradient descent method.A variety of simulation conditions are employed to analyze the yaw rate tracking accuracy,neural network generalization ability and adaptability of this proposed control algorithm in different vehicle load.(4)Research on driving force distribution control.For straight driving conditions,a driving force distribution strategy aiming at economical efficiency is designed,based on the energy efficiency characteristics of the electromotor.In the design,the genetic algorithm is used to optimize the distribution coefficient of the front-wheel driving force in an offline way,and China heavy-duty commercial vehicle test cycle is used to analyze this distribution strategy.For the steering conditions,an optimization strategy for driving force distribution aiming at cornering stability is proposed,the sequential quadratic programming algorithm is used to optimize the solution,and the distribution strategy is analyzed in simulation conditions.