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

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

Automated summary

The parameter identification of a Proton Exchange Membrane Fuel Cell (PEMFC) involves using optimization algorithms to determine unknown variables that are crucial for accurately predicting the fuel cell's performance. This study compared five optimization methods and found that the Bald Eagle Search (BES) algorithm achieved the lowest sum square error (SSE) of 0.035102, indicating accurate performance prediction. BES was identified as suitable for developing digital twins and control systems in the automotive industry. Additionally, BES demonstrated faster convergence compared to other algorithms. This research highlights the use of metaheuristic algorithms in fuel cell performance prediction for digital twin development in the automotive sector.