.png){kind=icon}
返回
Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.energy.2020.118738.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a new optimal method is proposed to select unknown parameters of the proton exchange membrane fuel cell (PEMFC) models. The method was based on minimizing the sum of squared error (SSE) value between the experimental output voltage and the estimated output voltage for the PEMFC stack. The minimization is based on employing a new improved design of the Barnacles Mating Optimization (IBMO) algorithm for increasing the system accuracy and robustness. The method is then validated based on two different case studies, including Horizon 500W PEMFC and NedSstack PS6 PEMFCs by comparing its results by the real data and also some well-known methods including Emperor Penguin Optimizer (EPO), Elephant Herding behavior Optimization (EHO) Algorithm, and world cup optimization algorithm (WCO). The results show that the suggested IBMO with 2.11 SSE has the minimum error and the EPO, EHO, and the WCO with 2.13, 2.26, and 2.29 SSE are in the next ranks. Also, for the Horizon 500W, the SSE value of the IBMO, EPO, WCO, and BMO are 0.012, 0.019, 0.029, and 0.031, respectively which shows the suggested method's superiority. Simulation results indicate that the suggested method has the best agreement with the empirical data. (c) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Proton exchange membrane fuel cell
Circuit-based model
Parameter identification
The sum of squared error
Improved barnacles mating optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.4
论文数:
4.3W
被引数:
20.2W
机构
引用论文
Thermo-economic modeling and analysis of an NG-fueled SOFC-WGS-TSA-PEMFC hybrid energy conversion system for stationary electricity power generation.png)
ENERGY
OAAI
ENERGYIF9.4
OAAI分享收藏Influence of Temperature on Vibrational Frequency of Graphene Sheet Used as Nano-Scale SensingCOAAI
CIF0
OAAI分享收藏Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization methodENERGY
ENERGYIF9.4
PREAI
分享收藏Biomimetic flow fields for proton exchange membrane fuel cells: A review of design trends质子交换膜燃料电池的仿生流场: 设计趋势综述ENERGYOAAI
ENERGYIF9.4
OAAI分享收藏
查看更多内容