Artificial intelligence-based multi-objective optimisation for proton
exchange membrane fuel cell: a literature review
Abstract
Proton exchange membrane fuel cells (PEMFCs) are promising devices for
conversing chemical energy into electrical energy due to their versatile
properties, such as high power density, quick start-up, lower operating
temperature, and 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
PEMFC field. Firstly, AI-MOO were introduced in detail, including the
definition, categories and framework. Then the objectives, intelligent
algorithms and trade-off methods that are commonly used in PEMFC were
tabularised and evaluated. The application of AI-MOO in PEMFC were
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.