Brushless DC Motor Control Strategy for Electric Vehicles

doi:10.23940/ijpe.18.03.p16.559566

Abstract

Abstract:

A self-adaptive fuzzy proportional integral derivative (PID) control method based on genetic optimization is proposed to solve the problem of low precision and low anti-jamming capabilities of the brushless direct current (DC) motor control system of electric vehicles. A double closed-loop speed control system model of the drive motor is established based on an analysis of the mathematic model of a permanent magnet brushless DC motor. Adaptive fuzzy PID control is introduced. The fuzzy membership function is optimized by the genetic algorithm and referred to as the optimized adaptive fuzzy PID control method. The design and simulation of the system are realized by using MATLAB/Simulink. Results show that in the same environment, the genetic algorithm with adaptive fuzzy PID control has better dynamic and static performance than ordinary and fuzzy PID. It has a good speed and anti-interference ability in a typical city driving environment.

Submitted on December 16, 2017; Revised on January 17, 2018; Accepted on February 20, 2018
References: 10

Wanmin Li, Xinyong Li, Yan Wang, Xianhao Zeng, and Yunzi Yang. Brushless DC Motor Control Strategy for Electric Vehicles [J]. Int J Performability Eng, 2018, 14(3): 559-566.

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References 0

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