Improved Dhole Optimization Algorithm for Optimal Parameter Estimation of PEMFC Models for High-Fidelity Energy Conversion

Proton Exchange Membrane Fuel Cells (PEMFCs) are crucial for the advancement of environmentally friendly hydrogen cars. It is one of the promising solutions for alternatives to conventional engines, primarily due to their ability to convert hydrogen into electricity. Fuel cell systems have a complex and non-linear mathematical model. Accurate identification of unknown parameters of the PEMFC mathematical model is an important aspect in energy conversion. This research intends to provide a novel meta-heuristic algorithm, which is known as the Improved Dhole Optimization Algorithm (IDOA), to estimate the unknown parameters of PEMFC models. The proposed IDOA is inspired by the collective hunting behavior of dholes. In this algorithm, candidate solutions are systematically arranged and dynamically updated to enhance the overall search process. The objective function is to minimize the sum of squared errors (SSE) between the actual and model-estimated voltages obtained using the proposed IDOA algorithm. In this research, three commonly known PEMFC benchmark models, NedStack PS6, BCS 500 W, and Horizon 500 W, are utilized to assess the performance of the IDOA algorithm. Also, the obtained results are compared against each other to validate their effectiveness. The comparative performance with an array of known optimization algorithms reported in the literature indicates that the IDOA algorithm has low estimation error, an excellent convergence rate, and superior robustness. Furthermore, these results support the appropriateness of the proposed IDOA algorithm for high-accuracy PEMFC modeling in energy conversion models.