Optimization of PEMFC Fuel Cell Parameters Using Metaheuristic Methods for Efficient and Sustainable Hydrogen Energy Production

Abstract

The increasing importance of renewable energies in the context of the global energy transition highlights the need to refine associated technologies, such as hydrogen fuel cells. To maximize their efficiency and reliability, it is crucial to accurately extract the empirical parameters of fuel cell models, including proton exchange membrane (PEM) models. This task is particularly challenging due to the non-linearity and complexity of the models used. Metaheuristic methods have emerged as promising solutions to address these challenges, offering enhanced capabilities for solving complex optimization problems. Among these methods, the Differential Evolution- Based Backtracking Search Algorithm (DEBSA) stands out for its ability to combine the strengths of two powerful approaches: the Backtracking Search Optimization Algorithm (BSA) and Differential Evolution (DE). DEBSA leverages BSA to guide the exploration of the search space based on past experiences, while DE accelerates convergence towards optimal solutions. This study assesses the effectiveness of DEBSA for parameter extraction in a complex BCS 500W fuel cell model, comparing it with five other well-established metaheuristic algorithms. The results show that DEBSA outperforms the other methods by achieving the lowest sum of squared errors (SSE) and the smallest standard deviation. These performances not only attest to DEBSA’s exceptional precision and stability but also its ability to provide accurate modeling of hydrogen fuel cell behavior. Overall, DEBSA proves to be a highly effective tool for optimizing the parameters of fuel cell models. Its superior performance compared to other algorithms demonstrates its potential to advance parameter estimation techniques and enhance the development of renewable energy technologies.