A Control-Oriented Model of a PEM Fuel Cell Stack Based on NARX and NOE Neural Networks

Abstract

Hydrogen-related technologies have been proposed as an alternative to store the energy surplus from renewable sources. Among these technologies, the proton exchange membrane fuel cell (PEMFC) and electrolyzer are the preferred choice for practical applications since they have reached a certain level of maturity and are commercially available at present. In order to achieve a cost-effective operation, a PEMFC stack must operate at maximum efficiency most of the time. Since PEMFC stacks present a time-varying behavior, an adaptive model-based controller should be employed to accomplish this goal. A fixed-parameter electrochemical model may not offer a reliable prediction over a midterm time horizon for such a controller. For this reason, system identification techniques appear as more appropriate choices to obtain an effective model for this class of control systems. In this paper, a system identification modeling methodology employing nonlinear autoregressive with exogenous input (NARX) and nonlinear output error (NOE) neural networks is presented to obtain a black-box model of a PEMFC stack oriented for a predictive control system. The experimental data for the model development are obtained with a commercial 3-kW PEMFC stack. The model built according to the proposed methodology provides accurate predictions of the voltage for the whole operating range of the stack for a long time and, hence, the ability to represent the time-varying behavior of a PEMFC stack for a predictive control application.