Hydrogen-Efficient Operation of a PEMFC-Powered Induction Motor Drive via Dhole-Inspired Optimization and a Neural Network Controller

This paper presents an energy-efficient operating strategy for a three-phase induction motor drive powered by a proton exchange membrane fuel cell (PEMFC). The proposed approach minimizes hydrogen consumption and improves motor efficiency over a wide operating range by continuously regulating the motor terminal voltage according to the load demand. The optimal reference voltage corresponding to each load torque is determined using the dholes-inspired optimization algorithm (DIOA), a recent swarm-based optimization technique. The DIOA is also employed to determine the optimal parameters of a proportional–integral (PI) controller, enabling accurate and rapid tracking of the reference voltage. To evaluate the effectiveness of the proposed optimizer, its performance is compared with the well-known particle swarm optimization (PSO) algorithm. The reference voltages obtained using the DIOA and PSO differ by less than 1.1%, their hydrogen consumptions differ by less than 0.02%, and a direct voltage sweep confirms the optimum. The dynamic performance is investigated using both an optimally tuned PI controller and a neural network controller (NNC). Simulation results show that the NNC provides faster settling, lower transient deviations, and improved dynamic performance compared with the PI controller. Steady-state results indicate that hydrogen savings are strongly dependent on motor loading. At a load torque of 0.5 N·m (5% of full load), hydrogen consumption is reduced from 1.999121 slpm to 0.706726 slpm, representing a saving of 64.65%. At a load torque of 2 N·m (20% of full load), the hydrogen saving reaches 24.17%. As the load approaches its rated value, the motor operates near its maximum efficiency and rated voltage.