A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Huu-Linh Nguyen, Sang-Min Lee and Sangseok Yu ()
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Huu-Linh Nguyen: Department of Mechanical Engineering, Graduate School, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
Sang-Min Lee: Department of Clean Fuel and Power Generation, Korea Institute of Machinery & Materials (KIMM), 156 Gajeongbuk-ro, Yuseong-gu, Daejeon 34103, Republic of Korea
Sangseok Yu: School of Mechanical Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea

Energies, 2023, vol. 16, issue 12, 1-32

Abstract: Proton exchange membrane fuel cells (PEMFCs) are an alternative power source for automobiles that are capable of being cleaner and emission-free. As of yet, long-term durability is a core issue to be resolved for the mass production of hydrogen fuel cell vehicles that requires varied research in the range from sustainable materials to the optimal operating strategy. The capacity to accurately estimate performance degradation is critical for developing reliable and durable PEMFCs. This review investigates various PEMFC performance degradation modeling techniques, such as model-based, data-driven, and hybrid models. The pros and cons of each approach are explored, as well as the challenges in adequately predicting performance degradation. Physics-based models are capable of simulating the physical and electrochemical processes which occur in fuel cell components. However, these models tend to be computationally demanding and can vary in terms of parameters between different studies. On the other hand, data-driven models provide rapid and accurate predictions based on historical data, but they may struggle to generalize effectively to new operating conditions or scenarios. Hybrid prediction approaches combine the strengths of both types of models, offering improved accuracy but also introducing increased computational complexity to the calculations. The review closes with recommendations for future research in this area, highlighting the need for more extensive and accurate prediction models to increase the reliability and durability of PEMFCs for fuel cell electric vehicles.

Keywords: PEMFC; performance degradation; lifetime prediction; hydrogen fuel cell vehicle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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