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

Pages : 2968-2985

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This paper introduces an optimal control strategy of model-based predictive control insert ignore into journalissuearticles values(MPC); based on multiobjective particle swarm optimization insert ignore into journalissuearticles values(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.
Keywords : Electric vehicle, fuel cell, sensorless vector control, multiobjective particle swarm optimization, model based predictive control