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

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., (eo, α, R, jeid, C1, β,req) 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

• Parameter extraction
• Mathematical modeling
• Sustainable development
• Optimization
• Non-parametric test