A systematic review of machine learning methods applied to fuel cells in performance evaluation, durability prediction, and application monitoring. (12th February 2023)

• Record Type: Journal Article
• Authors: Ming, Wuyi; Sun, Peiyan; Zhang, Zhen; Qiu, Wenzhe; Du, Jinguang; Li, Xiaoke; Zhang, Yanming; Zhang, Guojun; Liu, Kun; Wang, Yu; Guo, Xudong
• Is Part Of: International journal of hydrogen energy. Volume 48:Number 13(2023)
• Page Start: 5197
• Page End: 5228
• Publication Date: 2023-02-12
• Subjects: Fuel cells -- Performance evaluation -- Durability prediction -- Application monitoring -- Machine learning -- Deep learning; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2022.10.261
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: A fuel cell is a power generation device that directly converts chemical energy into electrical energy through chemical reactions; fuel cells are widely used in aerospace, electric vehicle, and small-scale stationary engine applications. The complex phenomena including mass/heat transfer, electrochemical reactions, and ion/electron conduction, can significantly affect the energy efficiency and durability of fuel cells, but are difficult to determine completely. Machine learning (ML) performs well in solving complex problems in engineering applications and scientific research. In this paper, a systematic review is conducted to explore ML methods, including traditional ML and deep learning (DL) methods, applied to fuel cells for performance evaluation (material selection, chemical reaction modeling, and polarization curves), durability prediction (state of health, fault diagnostics, and remaining useful life), and application monitoring. Then comparisons of traditional ML and DL methods are discussed, while the similarities and differences between ML and integrated physics simulations are also concluded. Eventually, the scope of ML methods applied to fuel cells is presented, and outlooks of future researches on ML applications in fuel cells are identified. Highlights: A systematical review is conducted to study on tradition ML and DL methods applied to fuel cells. Applications of ML in performance evaluation, durability prediction and application monitoring of fuel cells are analyzed. The comparisons of tradition ML and DL methods are investigated. Similarities and differences between ML and that integrated physics simulation are discussed. Scope of ML methods applied to fuel cells for local and global problems is presented.