Singla Manish Kumar, Gupta Jyoti, Nijhawan Parag, Alsharif Mohammed H, Kim Mun-Kyeom
Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India.
Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
Heliyon. 2023 Mar 14;9(3):e14578. doi: 10.1016/j.heliyon.2023.e14578. eCollection 2023 Mar.
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., ( , , , , , ,r) 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.
利用直接甲醇燃料电池（DMFC）堆栈的数学模型，提出了一种新的优化方法，用于最优估计七个未知参数，即( , , , , , ,r)。具体而言，基于模拟实验数据，提出了一种方法来最小化与估计极化曲线相关的平方误差之和（SSE）。增强向量加权均值（EINFO）算法是一种为实现这一目标而提出的新型元启发式方法。然后将该方法的结果分析与各种元启发式算法进行比较，如文献中熟知的粒子群优化（PSO）、正弦余弦算法（SCA）、蜻蜓算法（DA）、原子搜索优化（ASO）和向量加权均值（INFO）。作为确认所提方法有效性的最后一步，利用温度变化进行敏感性分析，并与文献中描述的不同方法进行比较以证明其优越性。在比较参数估计和不同工作温度后，还进行了非参数检验，并与手稿中使用的其他元启发式算法进行比较。从这些测试中得出结论，所提算法优于其他比较算法。
