Study On Modeling And Control Research Of Parallel Hybrid Electric Vehicle And Its Motor

In order to solve the increasingly severe energy crisis and environmental problems caused by the increasing per capita consumption of automobiles and the massive consumption of petroleum resources,vigorously developing clean and efficient new vehicles has become the top priority of the development strategy of the automobile industry at this stage.Compared with pure electric vehicles,hybrid vehicles not only have the advantages of low carbon efficiency,but also effectively solve the problem of insufficient cruising range.Compared with traditional internal combustion engines,the fuel ratio is higher,which greatly reduces exhaust emissions.Therefore,research on hybrid vehicle related technologies is of great significance.The main work of this paper is as follows:1.First,the structural characteristics of different types of hybrid vehicles are introduced.Then,according to the weight of the vehicle body,the radius of the tire and the like and the preset vehicle performance index,suitable performance parameters are matched for the transmission system components of the vehicle,such as an engine,a motor,a power battery,and the like.Parallel hybrid vehicles based on P2 motor architecture were modeled on the Matlab/Simulink platform.2.The parameters of the permanent magnet synchronous motor of one of the hybrid vehicle power components are identified.Based on the analysis of its mathematical model,this paper has improved the integrated learning particle swarm optimization algorithm and applied it to the parameter identification of permanent magnet synchronous motor.The simulation results show that compared with other algorithms under the same number of iterations,the algorithm can accurately identify the parameters of permanent magnet synchronous motor.At the same time,based on this result,the PMSM vector control system is built and a voltage feedforward compensation regulator is introduced in the current loop.The simulation results verify the effectiveness of the motor control strategy.3.The hardware design of the vehicle controller was carried out,and then the rule control strategy based on logic threshold was introduced.The control strategies such as vehicle power-on,vehicle torque distribution and vehicle mode switching were designed,and the above vehicles were simulated on the simulation platform.The control strategy and vehicle economy and dynamics were verified by offline simulation,which also laid the foundation for subsequent online testing.4.Hardware-in-the-loop simulation testing and real vehicle calibration were performed.The code is brushed into the vehicle controller,and a virtual vehicle test environment is formed with the hardware platform.The test bench simulates the information of the actual vehicle running,thereby realizing the initial online verification of the control strategy.Finally,the Canape calibration tool is used to read the real-time signal of the vehicle during real vehicle operation to achieve final verification of the vehicle control strategy.