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http://bura.brunel.ac.uk/handle/2438/29949| Title: | A review of control strategies for proton exchange membrane (PEM) fuel cells and water electrolysers: From automation to autonomy |
| Authors: | Mao, J Li, Z Xuan, J Du, X Ni, M Xing, L |
| Keywords: | PEMFC;PEMWE;control;management system;AI |
| Issue Date: | 28-Jul-2024 |
| Publisher: | Elsevier |
| Citation: | Mao, J. et al. (2024) 'A review of control strategies for proton exchange membrane (PEM) fuel cells and water electrolysers: From automation to autonomy', Energy and AI, 17, 100406, pp. 1 - 17. doi: 10.1016/j.egyai.2024.100406. |
| Abstract: | Proton exchange membrane (PEM) based electrochemical systems have the capability to operate in fuel cell (PEMFC) and water electrolyser (PEMWE) modes, enabling efficient hydrogen energy utilisation and green hydrogen production. In addition to the essential cell stacks, the system of PEMFC or PEMWE consists of four sub-systems for managing gas supply, power, thermal, and water, respectively. Due to the system's complexity, even a small fluctuation in a certain sub-system can result in an unexpected response, leading to a reduced performance and stability. To improve the system's robustness and responsiveness, considerable efforts have been dedicated to developing advanced control strategies. This paper comprehensively reviews various control strategies proposed in literature, revealing that traditional control methods are widely employed in PEMFC and PEMWE due to their simplicity, yet they suffer from limitations in accuracy. Conversely, advanced control methods offer high accuracy but are hindered by poor dynamic performance. This paper highlights the recent advancements in control strategies incorporating machine learning algorithms. Additionally, the paper provides a perspective on the future development of control strategies, suggesting that hybrid control methods should be used for future research to leverage the strength of both sides. Notably, it emphasises the role of artificial intelligence (AI) in advancing control strategies, demonstrating its significant potential in facilitating the transition from automation to autonomy. |
| URI: | https://bura.brunel.ac.uk/handle/2438/29949 |
| DOI: | https://doi.org/10.1016/j.egyai.2024.100406 |
| Other Identifiers: | ORCiD: Xinli Du https://orcid.org/0000-0003-2604-0804 ORCiD: Lei Xing https://orcid.org/0000-0002-0360-8025 100406 |
| Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( https://creativecommons.org/licenses/by/4.0/ ). | 8.49 MB | Adobe PDF | View/Open |
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