Improving fuel cell performance via optimal parameters identification through fuzzy logic based-modeling and optimization

Tanveer, Waqas Hassan; Rezk, Hegazy; Nassef, Ahmed; Abdelkareem, Mohammad Ali; Kolosz, Ben; Karuppasamy, K.; Aslam, Jawad; Gilani, Syed Omer

Abstract

Improving the performance of solid oxide fuel cell via maximizing its available peak power density is a key requirement of research in the field of renewable energy. This could be achieved through identifying the optimal controlling parameters such as, the deposition instrument power (P), the temperature (T), and the electrolyte thickness (Thick). Nickel–Gadolinium Doped Ceria cermet anode films are deposited on one side of the Zirconia electrolyte by radio frequency sputtering. The sputtering plasma power is varied at 50, 100, 150, and 200 W. Lanthanum Strontium Manganite cathodes were screen-printed on the other side of the electrolyte supports to complete the configuration. Cells were electrochemically tested at various intermediate solid oxide fuel cell temperatures of 600, 700 and 800 °C using different electrolyte thicknesses. The cell's current density, I (A/cm2) and voltage (V) and hence the power density (W/cm2) are recorded in each case. Based on the obtained experimental results, a fuzzy model is built using different control parameters. Then, the particle swarm optimization technique is used for obtaining the best parameters of the cell that maximizes its power density. The results show that by utilizing the optimized conditions, the power density can be increased to 0.39 (W/cm2), which is almost two times higher than the maximum power density obtained experimentally.

Citation

Tanveer, W. H., Rezk, H., Nassef, A., Abdelkareem, M. A., Kolosz, B., Karuppasamy, K., Aslam, J., & Gilani, S. O. (2020). Improving fuel cell performance via optimal parameters identification through fuzzy logic based-modeling and optimization. Energy, 204, Article 117976. https://doi.org/10.1016/j.energy.2020.117976

• Journal Article Type: Article
• Acceptance Date: May 25, 2020
• Online Publication Date: May 30, 2020
• Publication Date: Aug 1, 2020
• Deposit Date: Aug 3, 2024
• Journal: Energy
• Print ISSN: 0360-5442
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 204
• Article Number: 117976
• DOI: https://doi.org/10.1016/j.energy.2020.117976
• Public URL: https://hull-repository.worktribe.com/output/4057290