Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm

Xianke Sun, Gaoliang Wang, Liuyang Xu, Honglei Yuan and Nasser Yousefi

Energy, 2021, vol. 237, issue C

Abstract: The present study proposes a new efficient methodology for optimal model identification of the Proton-exchange membrane fuel cell (PEMFC) stacks based on an improved version of a Deep Belief Network (DBN). The proposed DBN has been updated by a new metaheuristic to provide the minimum relative error between the experimental output voltage and the network output data during simulation of the nonlinear transient behavior of the Proton-exchange Membrane Fuel Cells (PEMFC). To develop the effectiveness of the DBN, an improved version of the Archimedes optimization algorithm (IAOA) has been developed. The results of training and testing of the proposed method are compared with the original DBN model to indicate the method's effectiveness. Simulations showed 34.0879 and 28.5016 V for the DBN and the suggested DBN-IAOA methods, respectively. This indicates the higher performance of the suggested method toward the original DBN model and its well-organization for modeling the PEMFC stacks.

Keywords: Proton-exchange membrane fuel cells; Model estimation; Output voltage; Deep belief network; Improved archimedes optimization algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221017801
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:237:y:2021:i:c:s0360544221017801

DOI: 10.1016/j.energy.2021.121532

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().