Optimal Parameter Identification of a PEM Fuel Cell Using Recent Optimization Algorithms

The parameter identification of a PEMFC is the process using optimization algorithms to determine ideal unknown variables suitable for development an accurate fuel-cell-performance prediction model. These parameters are not always available from manufacturer’s datasheet, so they need be determined accurately model and predict fuel cell’s performance. Five methods—bald eagle search (BES) algorithm, equilibrium optimizer (EO), coot (COOT) antlion (ALO), heap-based (HBO)—are used compute seven PEMFC. During optimization, these as decision variables, fitness function minimized sum square error (SSE) between estimated cell voltage actual measured voltage. SSE obtained BES algorithm was noted 0.035102. COOT recorded 0.04155, followed by ALO with 0.04022 HBO 0.056021. predicted performance accurately; hence, it digital twin fuel-cell applications control systems automotive industry. Furthermore, deduced that convergence speed faster compared other investigated. This study aims use metaheuristic commercialization twins in