Research on Electric Vehicle DC Speed Regulation Based on PSO Optimization

In the field of transportation, new-energy vehicles have greatly promoted green and low-carbon development and have broad prospects. However, DC motors widely used in new energy vehicles still have difficulties determining control parameters in double closed-loop speed regulation, which may lead to unsatisfied control response. To aim at this problem, this paper proposes a PSO particle swarm optimization method to optimize the PID control parameters of the DC motor. Firstly, according to the non-linear and multi-variable characteristics of the brushless DC motor control system, we build the motor model then apply PID control theory based on the speed loop to realize the single closed-loop and double closed-loop speed control of the motor. Then, to improve the motor control effect and realize the self-tuning of PID parameters, the PSO particle swarm algorithm was introduced into the controller to tune the PID control parameters. After that, we use Simulink/Matlab to obtain the output curve and ITAE index of motor control before and after optimization and prove the improved self-tuning effect of the PSO particle swarm algorithm on PID parameters. Finally, we perform proteus simulation to complete the electric vehicle in different working conditions, such as high and low speed, acceleration-deceleration, and disturbance. The experimental results indicate the excellent engineering application value.