Nonlinear Performance Degradation Prediction of Proton Exchange Membrane Fuel Cells Using Relevance Vector Machine
Environmental issues, especially global warming due to the greenhouse effect, have become more and more critical in recent decades. As one potential candidate among different alternative “green energy” solutions for sustainable development, the proton exchange membrane fuel cell (PEMFC) has received extensive research attention for many years for energy and transportation applications. In this paper, an advanced self-adaptive relevance vector machine (RVM) has been developed and demonstrated to predict the performance degradation of PEMFCs.
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Cities with substantial population growth continue to encounter economic, social, and environmental challenges in their daily operations. This growth has led to public outcry demanding that societies curb their dependence on fossil fuel consumption to limit global warming. In fact, major cities’ usage of fossil fuels constitutes 75% of global energy resource use and accounts for 70% of global greenhouse gas emissions, despite occupying only approximately 5% of the planet’s total land mass. Rapid urbanization also contributes to multiple types of serious environmental pollutants (e.g., air, soil, and water), which affect the people’s health and the quality of life.
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