Research On Electric Vehicle Velocity Optimal Control Method Based On Intelligent Transportation

Under the background of the increasing global energy and environmental problems,energy saving and environmental protection are the themes of the development of the automotive industry.Around these themes,the most popular research direction at present is the intelligent and electrification of vehicles.In the intelligent transportation environment,in order to reduce the energy consumption of multiple vehicles,this paper takes several identical pure electric vehicles as the research object,and takes the instantaneous optimal control as the core of the research method,puts forward the electric vehicle velocity optimization control method based on Intelligent transportation.The control method is based on the vehicle's energy consumption model,comprehensively considers the influence of other vehicles and the signal phase and timing(SPAT)on the vehicle velocity.The control method determines the economic vehicle velocity in real time by instantaneous optimization algorithm,and reduces the energy consumption of multiple vehicles.The main work in this paper is as follows:Intelligent transportation model is built with Vissim software.Vissim software is redeveloped by component object model(COM).The information between the interior of the traffic model can be transmitted and shared in real time to realize the intelligentization of the traffic scene.The performance model of electric vehicle is established by Autonomie software.The internal principle of the performance model of electric vehicle is analyzed from the aspects of vehicle longitudinal dynamics model,battery model,motor model,torque coupler model,final transmission device model and wheel model.The energy flow of the performance model of electric vehicle is analyzed to pave the way for the establishment of the energy consumption model of electric vehicle.An instantaneous optimal control method of vehicle velocity based on intelligent transportation is proposed.Firstly,it is necessary to predict the velocity range of vehicles passing through the signal lights smoothly based on intelligent transportation information.Secondly,according to the energy flow of the electric vehicle performance model,the mathematical relationship between vehicle velocity and battery SOC is found,and the energy consumption model is established.Thirdly,according to the energy consumption model,the instantaneous optimization method is used to determine the economic velocity in real time from the predicted velocity range.In order to verify the effectiveness of the method proposed in this paper and make up for the deficiencies of the existing simulation platform,the Autonomie/Vissim joint simulation platform is built in this research.The joint simulation platform can study the simulation scenarios of multiple vehicles in intelligent transportation environment,and each vehicle has its own complete vehicle performance model.In addition,the simulation platform with Wiedemann carfollowing model can automatically control the safe distance between multiple vehicles and reduce the workload of the design control algorithm.In order to compare with the instantaneous optimal control method of vehicle velocity based on intelligent transportation information,three benchmark methods are proposed in this paper.All the velocity control methods are programmed in Python language and embedded in the joint simulation platform.Benchmark methods 1 and 2 do not consider the energy consumption model of vehicles,but consider other vehicles and SPAT.The simulation results show that the energy consumption of the method of this paper is reduced by 11.89%,8.68% and 40.05% compared with the benchmark methods 1,2 and 3,respectively,which proves the effectiveness of the method of this paper in energy saving.