Enhancing Performance of PEM Fuel Cell Powering SRM System Using Metaheuristic Optimization

This paper introduces an effective method to improve the performance of a proton exchange membrane fuel cell (PEMFC) system powering a switched reluctance motor (SRM). Problems arise in this system due to the inherent torque and current ripples of the SRM, which result from its saliency and nonlinear magnetic characteristics. Another cause for these ripples is the unsmoothed DC voltage applied to the SRM caused by the switching operations of the DC-DC converter. These ripples are reflected in the PEMFC, leading to more losses and a reduced lifespan. Key parameters that can help mitigate torque and current ripples include the appropriate turn-on and turn-off angles of the SRM phases, as well as the DC-link voltage controller gains. This paper investigates three objectives to compare their effects on the PEMFC system: the SRM torque ripple factor, the DC-link voltage ripple factor, and the PEMFC current ripple factor. These objectives are optimized individually using the single-objective particle swarm and stochastic fractal search algorithms. Additionally, the multi-objective Lichtenberg and multi-objective Dragonfly algorithms are applied to optimize the three objectives concurrently. The optimal decision parameters are obtained from the Pareto front solution using the technique of the order of preference by similarity to the ideal solution method. The final results demonstrate that significant enhancement in the PEMFC current ripples and DC-link voltage ripples can be achieved by appropriately selecting the decision parameters using any proposed objective.