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Brushless DC Motor Control Strategy for Electric Vehicles

Volume 14, Number 3, March 2018, pp. 559566
DOI: 10.23940/ijpe.18.03.p16.559566

Wanmin Lia, Xinyong Lib, Yan Wanga, Xianhao Zenga, and Yunzi Yanga

aSchool of Automobile Engineering, Lanzhou Institute of Technology, Lanzhou, 730050, China
bSchool of Mechanical Engineering, Changshu Institute of Technology, Suzhou, 215500, China

(Submitted on December 16, 2017; Revised on January 17, 2018; Accepted on February 20, 2018)


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.


References: 10

  1. A. Darba, F. D. Belie , P. D. Haese and J. A. Melkebeek, “Improved Dynamic Behavior in BLDC Drives Using Model Predictive Speed and Current Control,” IEEE Transactions on Industrial Electronics, vol. 63, no. 2, pp. 728–740, 2016
  2. A. A. Fahmy and A. M. A. Ghany, “Adaptive functional-based neuro-fuzzy PID incremental controller structure,” Neural Computing and Applications, vol. 26, no. 6, pp. 1423–1438, 2015
  3. J. Li and Y. Zhong, “Robust speed control of induction motor drives using first-order auto-disturbance rejection controllers,” IEEE Industry Applications Society Meeting, vol. 51, no. 1, pp. 712–720, 2015
  4. W. M. Li, L. M. Gu and L. L. Wei “Speed Control Simulation of the Electric Vehicle Driving Motor,” International Journal of Performability Engineering, vol. 13, no. 7, pp. 1140-1146, 2017
  5. C. Navaneethakkannan and M. Sudha, “Analysis and Implementation of ANFIS-based Rotor Position Controller for BLDC Motors,” Journal of Power Electronics, vol. 16, no. 2, pp. 564–571, 2016
  6. V. K. S. Patel and A. K. Pandey, “Modeling and Simulation of Brushless DC Motor Using PWM Control Technique,” Internation-al Journal of Engineering Research and Applications, vol. 3, no. 3, pp. 612-620, 2013
  7. A. Pandian and R. Dhanasekaran, “Hybrid Anti-Windup Fuzzy PI Controller Based Direct Torque Control of Three Phase Induction Motor,” Applied Mechanics and Materials, vol. 3230, no.573, pp. 155–160, 2014
  8. A. L. Saleh and A. A. Obed, “Speed Control of Brushless DC Motor based on Fractional Order PID Controller,” International Journal of Computer Applications, vol. 95, no. 4, pp. 1-6, 2014.
  9. T. Vijayakumar, S. Muthukrishnan and G. Murugananth, “Genetic Algorithm Based Speed Control of PMDC Motor Using Low Cost PIC 16F877A Microcontroller,” Circuits and Systems, vol. 7, no. 8, pp. 1334-1340, 2016.
  10. H. Yau, P. Yu and Y. h. Su, “Design and Implementation of Optimal Fuzzy PID Controller for DC Servo Motor,” Applied Mathematics & Information Sciences, vol. 8, pp. 231-237, 2014


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