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

• Published in: IEEE transactions on industry applications Vol. 52; no. 3; pp. 2573 - 2581
• Main Authors: Yiming Wu, Breaz, Elena, Fei Gao, Miraoui, Abdellatif
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Aging; Automatic; Byproducts; Degradation; Electric power; Engineering Sciences; Fluid mechanics; Fuel cells; Hydrogen; Kernel; Machine learning; Mathematical models; Mechanics; Performance degradation; Physics; Support vector machines; Thermics; Training; Aging; fuel cells; nonlinear estimation; error analysis; energy resources
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2016.2524402

Proton 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.