Asset Details
Do you wish to reserve the book?
Adaptive Estimation of Quasi-Empirical Proton Exchange Membrane Fuel Cell Models Based on Coot Bird Optimizer and Data Accumulation
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Adaptive Estimation of Quasi-Empirical Proton Exchange Membrane Fuel Cell Models Based on Coot Bird Optimizer and Data Accumulation
Do you wish to request the book?
Adaptive Estimation of Quasi-Empirical Proton Exchange Membrane Fuel Cell Models Based on Coot Bird Optimizer and Data Accumulation
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Adaptive Estimation of Quasi-Empirical Proton Exchange Membrane Fuel Cell Models Based on Coot Bird Optimizer and Data Accumulation
2023
Overview
The ambitious spread of fuel cell usage is facing the aging problem, which has a significant impact on the cells’ output power. Therefore, it is necessary to develop reliable techniques that are capable of accurately characterizing the cell throughout its life. This paper proposes an adaptive parameter estimation technique to develop a robust proton exchange membrane fuel cell (PEMFC) model over its lifespan. This is useful for accurate monitoring, analysis, design, and control of the PEMFC and increasing its life. For this purpose, fair comparisons of nine recent optimization algorithms were made by implementing them for a typical quasi-empirical PEMFC model estimation problem. Investigating the best competitors relied on two conceptual factors, the solution accuracy and computational burden (as a novel assessment factor in this study). The computational burden plays a great role in accelerating the model parameters’ update process. The proposed techniques were applied to five commercial PEMFCs. Moreover, a necessary statistical analysis of the results was performed to make a solid comparison with the competitors. Among them, the proposed coot-bird-algorithm (CBO)-based technique achieved a superior and balanced performance. It surpassed the closest competitors by a difference of 16.01% and 62.53% in the accuracy and computational speed, respectively.
Publisher
MDPI AG
Subject
/ Aging
