Parameter estimation of NedStackPS6 PEM fuel cell using Newton-Raphson-based optimizer

Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising technology for mobility applications due to high efficiency, clean emissions and rapid refueling. Developing real-time systems using PEMFCs requires an accurate model with well-defined parameters for design, simulation, and performance evaluation. The paper proposes a new metaheuristic optimization technique, the Newton-Raphson-based optimizer (NRBO) algorithm, for accurate estimation NedStackPS6 fuel cell model parameters. The proposed NRBO algorithm combines ideas from gradient-based and population-based optimization techniques for accurate and efficient parameter estimation. NRBO algorithm bridges the gap between gradient-based and population-based methods. The NRBO algorithm harnesses the exploration efficiency of population-based methods while incorporating gradient information to guide the search towards promising regions within the feasible space. The proposed algorithmic framework for PEMFC parameter estimation is developed based on the Newton-Raphson method for root finding. For accurate estimation of the optimal model parameters for NedStackPS6 PEMFC, NRBO minimizes the total squared error between the estimated and the measured and fuel cell voltage across various data points to find optimal model parameters. NRBO's performance is evaluated by comparing its results to popular optimization algorithms. The statistical comparisons show that NRBO outperforms existing algorithms in accuracy, search capability, and convergence speed.