A review of control strategies for proton exchange membrane(PEM)fuel cells and water electrolysers:From automation to autonomy  

作　　者：Jiahao Mao Zheng Li Jin Xuan Xinli Du Meng Ni Lei Xing 

机构地区：[1]School of Chemistry and Chemical Engineering,University of Surrey,GU27XH,United Kingdom [2]Department of Building and Real Estate,Research Institute for Sustainable Urban Development(RISUD)&Research Institute for Smart Energy(RISE),The Hong Kong Polytechnic University,Hung Hom,Kowloon,China [3]Department of Mechanical and Aerospace Engineering,Brunel University London,UB83PH,United Kingdom

出　　处：《Energy and AI》2024年第3期470-486,共17页能源与人工智能（英文）

基　　金：support received from UK EPSRC under grant numbers EP/W018969/2,EP/V042432/1 and EP/V011863/2;the Leverhulme Trust under grant number PLP-2022-001.

摘　　要：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.

关 键 词：PEMFC PEMWE Control Management system AI 

分 类 号：TM911.4[电气工程—电力电子与电力传动]