MOPSO-based predictive control strategy for efficient operation of sensorless vector-controlled fuel cell electric vehicle induction motor drives

Authors

ADEL ABDELAZIZ ABDELGHANY ELGAMMAL
MOHAMMED FATHY EL_NAGGAR

Abstract

This paper introduces an optimal control strategy of model-based predictive control (MPC) based on multiobjective particle swarm optimization (MOPSO) for a sensorless vector control induction motor, which is used in a fuel cell electric vehicle drive system. The proposed MPC-MOPSO algorithm is implemented to tune the weighting parameters of the MPC controller to tackle all the conflicting objective functions. The paper handles the following fitness functions: minimizing the speed error, minimizing the torque ripple, minimizing the DC-link voltage ripple, and minimizing machine flux ripple. Computer simulations studies have been completed utilizing MATLAB/Simulink with a specific end goal of assessing the dynamic performance of the proposed MPC-MOPSO optimal controller and comparing it with single-objective particle swarm optimization and traditional PI controllers. The simulation results demonstrate the good dynamic response of the proposed MPC-MOPSO optimal tuning strategy over the traditional PI controllers for more accurate tracking performance through the whole speed range, especially at starting conditions and load change disturbances.

DOI

10.3906/elk-1608-115

Keywords

Electric vehicle, fuel cell, sensorless vector control, multiobjective particle swarm optimization, model-based predictive control

First Page

2968

Last Page

2985

Recommended Citation

ELGAMMAL, ADEL ABDELAZIZ ABDELGHANY and EL_NAGGAR, MOHAMMED FATHY (2017) "MOPSO-based predictive control strategy for efficient operation of sensorless vector-controlled fuel cell electric vehicle induction motor drives," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 25: No. 4, Article 35. https://doi.org/10.3906/elk-1608-115
Available at: https://journals.tubitak.gov.tr/elektrik/vol25/iss4/35