A hybrid pulse vibration high-frequency voltage signal injection method is proposed to solve the problems that the conventional sensorless control algorithm of vehicle IPMSM may generate a large estimated rotor position error and opposite directions in identifying the polarity of magnetic poles under zero-speed and high-torque starting and low-speed operation. The magnetic pole polarity is identified by the saturation effect of the flux chain by injecting a high-frequency sinusoidal voltage signal and opposite pulse voltage signal into the axis d ^ of the assumed coordinate system simultaneously. Subsequently, the position relationship between the assumed d ^ axis and the actual d axis is studied in accordance with the amplitude of response current to acquire the rotor position and speed information. The simulation and experimental results suggest that the algorithm is capable of accurately identifying the magnetic pole polarity and estimating the rotor position at zero speed and low speeds, starting the motor smoothly at zero speed, and then operating the motor stably at low speeds.

To solve the problems of the vehicle following model mismatch under different driving conditions and poor vehicle following performance, this paper proposes an adaptive cruise control system based on BP neural network to identify different driving conditions. Firstly, eight typical driving cycles are selected as samples, which are redivided into low-speed mode and medium high-speed mode. According to the selected characteristic parameters and re-division of driving mode, a new BP neural network structure is designed. Secondly, the characteristic parameter values are calculated by using the characteristic parameters, and the characteristic parameters are normalized. The working condition recognition model is obtained by training the normalized data values. Finally, according to the classification of working conditions, the fuzzy control upper control algorithm and the MPC upper control algorithm for medium-high speed conditions are designed respectively. Based on the above control algorithms, the joint simulation is carried out with Car-Sim and MAT-LAB/Simulink. The simulation results show that the low-speed fuzzy logic algorithm and the medium high-speed MPC algorithm proposed in this paper can make the vehicle have a better following performance and comfort.

In motor control, the stable control of motor speed and the stability in the face of sudden torque are two important indicators to evaluate the quality of the control system. This paper proposes a multi-island genetic algorithm (MIGA) and fuzzy PI vector control algorithm. Combined with the asynchronous motor control method, the simulation test and bench test are carried out. Taking the Integral Time Multiplied by the Absolute Value of Error (ITAE) minimum as the optimization goal, the MIGA optimization algorithm is used to obtain the optimal solution for the quantization factors ke0, kec0 , proportional factors kup, kui of the fuzzy PI controller. The optimal parameters are imported into the asynchronous motor control model for simulation verification and bench test verification. The simulation test shows that the fuzzy PI control method optimized by MIGA can effectively reduce the speed rise time, overshoot and steady-state error. When the load is suddenly applied, the speed drop is 5.16 r/min, and the adjustment time is 0.015s. The bench test shows that: under the changing load, the change trend of the speed curve of the simulation and the test is basically the same, which proves the correctness and effectiveness of the algorithm proposed in this paper.

The test bench is designed for the magneto rheological fluid brake, and its transmission scheme is that the motor transmit power to magnetorheological fluid brake from flywheels, in which the motor as the power source of the drive system affects the test bench performance, so it's key to design appropriate test bench drive system. First of all, the second expression is derived by the deformation of the motor torque formula (no frequency control); then draw test bench acceleration characteristic curve and determine the gantry acceleration time by integrating; ultimately, determine the motor model and gearbox transmission ratio, complete the design of drive system.