Research on Control Algorithm of Electric Energy Vehicle Based on Improved Gray Wolf Optimization Algorithm

With the rapid development of the automobile industry caused by environmental pollution, shortage of oil and other important resources and other problems are increasingly serious. Electric new energy vehicles have broad space development, and will become the mainstream direction of China’s automobile industry. However, the traditional servo brushless DC motor is difficult to control due to its low motor efficiency, power factor and power density, so it is difficult to use it as the driving motor of new energy vehicles. Therefore, an improved Grey Wolf Optimization Algorithm to optimize the PID control algorithm for new energy vehicles is proposed in this paper. Firstly, according to the nonlinear and multi-variable characteristics of the brushless DC motor control system, the motor is modeled and analyzed. Then the speed control of the motor is realized by using the PID method based on the speed loop, and the speed control performance is greatly improved by using the improved Gray Wolf Optimization algorithm(GWO)to adjust the PID control method in the controller. Finally, the performance of the enhanced Gray Wolf Optimization algorithm in motor speed PID control system is proved by Simulink and MATLAB simulation. Through simulation, it can be found that the control effect of the improved Gray Wolf Optimization algorithm optimized PID control algorithm is significantly higher than the particle swarm optimization algorithm, genetic algorithm and artificial trial and error method. The short rise time of step response, fast response speed, and small overshoot indicate that the algorithm has strong robustness and adaptability and has great engineering value.