Uncertainty analysis for the design, operation, and management of proton exchange membrane fuel cells

Abstract

Proton exchange membrane fuel cell (PEMFC) holds great promise due to its zero carbon emission and high energy conversion efficiency. However, improper design and operation can cause performance failures and accelerated degradation. A key challenge in modeling-based optimization is accounting for inherent randomness and operating disturbances, which calls for predictive models that incorporate uncertainty. In recent years, uncertainty analysis using advanced statistical and machine learning techniques has attracted growing attention for guiding the development of energy systems. Despite this progress, comprehensive reviews that link uncertainty analysis to performance enhancement of PEMFCs and their integrated power systems remain limited; existing PEMFC reviews mainly emphasize mechanistic, data-driven, and control-oriented modeling, while the crucial role of uncertainty analysis in these frameworks is only briefly addressed. This review summarizes PEMFC modeling approaches and uncertainty analytical techniques and keeps the reader abreast of recent advances of uncertainty analysis in the designs and management of PEMFCs. Moreover, this review discusses limitations in reliability and interpretability on uncertainty analysis and proposes directions for future research to construct more robust and trustworthy optimization strategies. Ultimately, this review aims to provide a comprehensive guide for applying uncertainty analysis to improve the design, control, and long-term stability of PEMFC systems.

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ORCID for Jeng Yi Chong: orcid.org/0000-0002-0593-6313

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