Parameter Estimation of Proton Exchange Membrane Fuel Cells Using Chaotic Newton-Raphson-Based Optimizer
Mahmoud S. AbouOmar, Ahmed Eltayeb, Maged S. Al-Quraishi, Sami El Ferik
Abstract
• An improved chaotic version of the Newton-Raphson-based optimizer (NRBO) algorithm is proposed and validated. • Chaotic maps are used to define the probability of the Trap Avoidance Operator (TAO) for the Newton-Raphson-based optimizer (NRBO). • The proposed Chaotic Newton-Raphson-based optimizer (CNRBO) is employed for extracting the parameters of PEMFC stacks. • Eight PEMFC stacks are used including NedStackPS6, BCS 500W, Horizon 500W, 250W stack, Avista SR-12 500W, Temasek 1KW, Ballard Mark V 5KW and Horizon H-1000XP stack. • CNRBO achieved the highest average accuracy as well as optimal accuracy for parameters extraction of PEMFCs. This paper presents a novel improved metaheuristic algorithm, Chaotic Newton-Raphson-Based Optimizer (CNRBO), for the Proton Exchange Membrane Fuel Cells (PEMFCs) parameter extraction problem. In this study, ten distinct chaotic maps are integrated within the Newton-Raphson-based optimizer (NRBO) to improve its performance. The chaotic maps are employed to define the probability of the trap avoidance operator (TAO). The proposed CNRBO algorithm is validated using standard benchmark optimization problems. Results proved its advantages over the standard NRBO algorithm in terms of accuracy, robustness, and convergence speed. For PEMFC parameters extraction using the proposed CNRBO algorithm, the objective function to be minimized is the sum of squared errors (SSE) between estimated and measured stack voltages across various data points. To validate the effectiveness and reliability of the proposed CNRBO, its performance is compared with recent algorithms on eight different PEMFC stack models including NedStackPS6, BCS 500W, Horizon 500W, 250W stack, Avista SR-12 500W, Temasek 1KW, Ballard Mark V 5KW and Horizon H-1000XP stack. Statistical measures are employed to assess the superiority and robustness of the proposed CNRBO. Statistical analyses demonstrate that the proposed CNRBO algorithm outperforms existing algorithms in terms of accuracy, search capability, and convergence speed, solidifying its position as a powerful tool for PEMFC parameter estimation.