Sustainable development of fuel cell using enhanced weighted mean of vectors algorithm

Manish Kumar Singla; Jyoti Gupta; Parag Nijhawan; Mohammed H Alsharif; Mun-Kyeom Kim

doi:10.1016/j.heliyon.2023.e14578

Sustainable development of fuel cell using enhanced weighted mean of vectors algorithm

Heliyon. 2023 Mar 14;9(3):e14578. doi: 10.1016/j.heliyon.2023.e14578. eCollection 2023 Mar.

Authors

Manish Kumar Singla¹, Jyoti Gupta², Parag Nijhawan², Mohammed H Alsharif³, Mun-Kyeom Kim⁴

Affiliations

¹ Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India.
² Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
³ Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea.
⁴ School of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, South Korea.

Abstract

Using the mathematical model of a Direct Methanol Fuel Cell (DMFC) stack, a new optimum approach is presented for estimating the seven unknown parameters i.e., ( $e_{o}$ , $α$ , $R$ , $j_{e i d}$ , $C_{1}$ , $β$ ,r_eq) optimally. Specifically, a method is proposed for minimization of the Sum of Squared Errors (SSE) associated with the estimated polarization profile, based on the experimental data from simulations. The Enhanced Weighted mean of vectors (EINFO) algorithm is a novel metaheuristic method that is proposed to achieve this goal. An analysis of the results of this method is then compared to various metaheuristic algorithms such as the Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Dragonfly Algorithm (DA), Atom Search Optimization (ASO), and Weighted mean of vectors (INFO) well known in literature. As a final step to confirm the proposed approach's effectiveness, the sensitivity analysis is carried out using temperature changes, along with comparison against different approaches described in the literature to demonstrate its superiority. After comparison of parameter estimation and different operating temperature a non-parametric test is also performed and compared with the rest of the metaheuristic algorithms used in the manuscript. From these tests it is concluded that the proposed algorithm is superior to the rest of the compared algorithms.

Keywords: Mathematical modeling; Non-parametric test; Optimization; Parameter extraction; Sustainable development.