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Machine learning based fault detection and state of health estimation of proton exchange membrane fuel cells

能源与人工智能(英文) 2023年第2期12卷 178-186页

作者： Vijay Mohan Nagulapati sshiva kumar Vimalesh Annadurai Hankwon Lim School of Energy and Chemical Engineering Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan44919Republic of Korea Carbon Neutrality Demonstration and Research Center Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan44919Republic of Korea Department of Mechatronics Rajalakshmi Engineering CollegeChennaiTamilnaduIndia Graduate School of Carbon Neutrality Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan44919Republic of Korea

In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells.

关键词： PEM fuel cell Data driven prognostics Fault detection Dynamic load test Gaussian process regression Support vector machine Artificial neural networks

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