摘要：Economic factors along with legislation and policies to counter harmful pollution apply specifically to maritime drive research for improved power generation and energy storage. Proton exchange membrane fuel cells are considered among the most promising options for marine applications. Switching converters are the most common interfaces between fuel cells and all types of load in order to provide a stable regulated voltage. In this paper, a method using artificial neural networks (ANNs) is developed to control the dynamics and response of a fuel cell connected with a DC boost converter. Its capability to adapt to different loading conditions is established. Furthermore, a cycle-mean, black-box model for the switching device is also proposed. The model is centred about an ANN, too, and can achieve considerably faster simulation times making it much more suitable for power management applications.