Artificial intelligence-based multi-objective optimisation for proton exchange membrane fuel cell: a literature review

Zhiming Feng, Jian Huang, Shan Jin, Guanqi Wang, Yi Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Proton exchange membrane fuel cells (PEMFCs) are promising devices for converting chemical energy into electrical energy due to their versatile properties, such as high power density, quick start-up, lower operating temperature, portability, etc. For PEMFC technology to outperform the incumbent technologies, artificial intelligence (AI) based multi-objective optimisation (AI-MOO) has been employed to facilitate the design and applications of PEMFC since AI-MOO is flexible enough to consider various factors simultaneously in the customized multiple objective functions and under new or updated case situations. This review provides a comprehensive literature survey on AI-MOO employed in the PEMFC field. Firstly, AI-MOO were introduced in detail, including the definition, categories and framework. Then the objectives, intelligent algorithms and trade-off methods commonly used in PEMFC were tabularised and evaluated. The application of AI-MOO in PEMFC was summarised systematically based on the application areas, including the PEMFC components, kinetics and thermodynamics, control and monitoring systems, the overall performance, and the hybrid systems. The related studies were tabularised and discussed, especially algorithms, variables, objectives and optimisation results. Finally, this review addressed the current challenges in the research area and proposed research implications for future investigations.

Original languageEnglish
Article number230808
Number of pages16
JournalJournal of Power Sources
Early online date26 Nov 2021
Publication statusPublished - 1 Feb 2022
Externally publishedYes


  • algorithms
  • artificial intelligence (AI)
  • multi-objective optimisation (MOO)
  • proton exchange membrane fuel cells (PEMFCs)


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