Ant colony optimization control of brushless DC motor driving a hybrid electric bike and fed from photovoltaic generator
The aim of this work is to design speed and current controllers of a brushless dc (BLDC) motor to drive a hybrid electric bike. The system is fed from two hybrid sources for driving the motor and charging of storage elements; one is a photovoltaic (PV) generator as a green and neat source; and the other is a human powered pedal dc generator. The proposed design of the controllers is formulated as an optimization problem to overcome the most static and dynamic fluctuations of the system. The ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral derivative (PID) parameters of the proposed controllers by minimizing the time domain of the objective function. The performance of the system is analysed when using the proposed controller with and without storage elements. Extensive simulation results are provided to validate the effectiveness and robustness of the proposed approach against system dynamics and PV fluctuations. The obtained results confirm the better performance of the system with the proposed controllers for several speed trajectories of the drive compared to the classical PID controllers.
2016 IEEE Congress on Evolutionary Computation (CEC)