Multiple subpopulation Salp swarm algorithm with Symbiosis theory and Gaussian distribution for optimizing warm-up strategy of fuel cell power system

Renkang Wang, Kai Li, Peng Chen and Hao Tang

Applied Energy, 2025, vol. 393, issue C, No S0306261925007809

Abstract: Swarm optimization algorithms have become a research hotspot for solving multiple parameter optimization problems in fuel cell systems to enhance hydrogen usage efficiency. However, the startup warming strategy is highly complex due to stage-wise constraints, resulting in slow convergence and suboptimal outcomes with conventional algorithms. Given that, this work innovatively proposes a multiple subpopulation division mechanism. It introduces symbiosis theory and Gaussian distribution to improve the basic Salp swarm algorithm, enhancing its local search and global exploitation capabilities when considering multiple constraints. First, a startup-warming model is developed to characterize the fuel cell temperature variation patterns during the energy conversion. Then, the optimization objective function is constructed, incorporating complex stage-wise restrictive conditions to reveal the energy consumption mechanism and identify the limiting factors of the warming strategy. Finally, the improved Salp swarm algorithm facilitates the efficient and reliable identification of the optimal warming strategy to minimize energy consumption. Experimental results demonstrate that compared to the basic algorithm, the proposed method reduces energy consumption by up to 6.41 %, 4.25 %, and 4.88 % under startup duration, initial temperature, and target temperature constraints. The multiple subpopulation Salp swarm algorithm demonstrates excellent performance and significant advantages in optimizing fuel cell energy efficiency.

Keywords: Proton exchange member fuel cell; Startup procedure; Warm-up strategy; Energy consumption optimization; Salp swarm algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925007809
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:appene:v:393:y:2025:i:c:s0306261925007809

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.126050

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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