A Review of Machine Learning Applications in Hydrogen Electrochemical Devices

Machine learning methods have been proven to be a useful tool for solving complex problems based on historical data in both scientific and engineering applications. Those properties make them a great candidate for providing a better insight into the operating characteristics of hydrogen electrochemical devices such as electrolyzers and fuel cells. Therefore, this paper critically analyzes the current state of research on the application of machine learning methods for predicting operating parameters, degradation detection with an emphasis on diagnostics and prognostics, and fault detection in hydrogen electrochemical devices. The analysis includes a comparison of different methods, discussion of existing challenges, and exploration of future potential applications. Addition ally, guidelines for future research, along with recommendations and best practices for applying machine learning methods, are provided.