A Modified Relevance Vector Machine for PEM Fuel-Cell Stack Aging Prediction

International audienceProton exchange membrane fuel cells (PEMFCs) are considered as a potential candidate in the green-energy applications in the near future. Comparing with other energy options, the PEMFCs need only hydrogen and air during operation. Meanwhile, as a by-product during operation, water is produced. This energy-conversion process is 100% eco-friendly and completely unharmful to the environment. However, PEMFCs are vulnerable to the impurities of hydrogen or fluctuation of operational condition, which could cause the degradation of output performance over time during operation. Thus, the prediction of the performance degradation is critical to the PEMFC system. In this work, a novel PEMFC performance-forecasting model based on a modified relevance vector machine (RVM) has been proposed, followed by a comparison with the approach of classic support vector machine (SVM). First, the theoretical formulation of RVM is briefly introduced, then the implementation steps of RVM using the experimental aging data sets of PEMFC stack output voltage are presented. By considering the specific feature of aging data-prediction problem, an innovative modified RVM formulation is proposed. The results of proposed modified RVM method are analyzed and compared to the results of SVM. The results have demonstrated that the modified RVM can achieve better performance of prediction than SVM, especially in the cases with relatively small training data sets. This novel method based on modified RVM approach has been demonstrated to show its effectiveness on forecasting the performance degradation of PEMFCs

A Review of physics-based and data-driven models for real-time control of polymer electrolyte membrane fuel cells

The real-time model-based control of polymer electrolyte membrane (PEM) fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions, involving the pressure, temperature, humidity, and stoichiometry ratio. In this article, recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed. The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described. The reduced-dimensional physics-based models (pseudo-two-dimensional, one-dimensional numerical and zero dimensional analytical models) and the non-physics-based models (zero-dimensional empirical and data-driven models) have been systematically examined, and the comparison of these models has been performed. It is found that the current trends for the real-time control models are (i) to couple the single cell model with balance of plants to investigate the system performance, (ii) to incorporate aging effects to enable long-term performance prediction, (iii) to increase the computational speed (especially for one-dimensional numerical models), and (iv) to develop data-driven models with artificial intelligence/machine learning algorithms. This review will be beneficial for the development of physics or non-physics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.Toyota Motor Engineering & Manufacturing North America || Natural Sciences and Engineering Research Council of Canad