Research On The Evaluation Of Drivability And Multi-Objective Optimal Control Of Electric Vehicles

Facing the global energy crisis and environmental degradation,electric vehicles have become the industry's development trend due to their advantages of low energy consumption and low emissions.Drivability is one of the dynamic characteristics of a vehicle during driving,and the proportion of drivability gradually increases in various performance indicators of electric vehicles.At present,electric vehicle control research is mostly focused on improving power and improving energy efficiency,and there are few studies on driving performance and its coupling relationship with power and economy and control laws.The driving,economy and power of the electric vehicle are determined by the driving strategy and shift strategy of the electric vehicle.To coordinate the optimization of the three,it is necessary to adjust the control strategy and the shift strategy in the optimization process.In this paper,based on the characteristics of electric vehicle motor drive,a parameterized adjustment method of electric vehicle control strategy and shift gauge strategy is established.On this basis,the multi-objective optimal control of electric vehicle's drivability,economy,and power is studied.The main research contents of this article are as follows:(1)Establish parameterized adjustment methods for electric vehicle drive strategy and shift strategy.The electric vehicle control strategy is expressed as a curve of torque load coefficient varying with accelerator pedal opening degree,the torque load coefficient under the economic driving strategy is calculated,and the torque load coefficient under the dynamic driving strategy is calculated.Based on the two driving strategies,the load adjustment factor K1 is introduced to establish the relationship between the adjusted torque load factor and the load adjustment factor,dynamic torque load factor and economic torque load factor.Using a similar method,based on the parameterization adjustment method of the drive strategy,the load adjustment factor K2 is introduced to establish the parameterization adjustment method of the shift strategy.(2)Establish an objective evaluation method for electric vehicles.The typical working conditions of electric vehicle drivability evaluation are analyzed,and the objective evaluation indicators characterizing electric vehicle drivability are selected.Based on BP neural network,a driving neural network evaluation model is established.Select the objective evaluation indexes of power and economy of pure electric vehicles,and establish the objective evaluation methods of economy and power.(3)Complete vehicle model construction.Establish Matlab /Simulink simulation model of pure electric vehicle matching CVT,including: driver model,driving strategy model,driving motor model,power battery model,CVT shift strategy model,transmission system model,vehicle longitudinal dynamics model,objective score Models(driving neural network model,dynamics,economy).(4)Multi-objective optimal control of Pareto principle.Based on the vehicle model,drive strategy and shift strategy parameterized adjustment method,the corresponding working conditions are selected for simulation research.The Pareto optimal solution set is studied,and K1 and K2 are adjusted during the simulation to obtain the optimal control surface of Pareto optimal control surface and load adjustment factors K1 and K2.Achieve multi-objective optimal control between the driving performance,economy and power of electric vehicles.