Rapid (practical) methodology for creation of fuel cell systems models with scalable complexity

Usually, knowledge-based modeling a system this complex takes several years to accomplish and still does not take nuisance factors into account. In contrast, the approach presented here can be finished within a fraction of that time. We propose to employ black-box adaptive modeling; the key issue in here, selecting an appropriate set of input features, can be solved by either applying iterative wrapper methods, or by making use of the automatic relevance detection technique that has been developed earlier within the framework of Bayesian neural networks. These procedures allow to easily scale the complexity of models in order to accommodate different constraints in terms of modeling effort, sensor availability and cost, and required model accuracy. Our approach can as well be used for the development of diagnostic models for on- and off-board diagnostics.