Enhancing the efficiency and lifetime of a proton exchange membrane fuel cell using nonlinear model predictive control with nonlinear observation
Journal Article (2017)
IEEE Transactions on Industrial Electronics
The aim of this research is to develop and test in a simulation environment an advanced model-based control solution for a Proton Exchange Membrane Fuel Cell (PEMFC) system. A Nonlinear Model Predictive Control (NMPC) strategy is proposed to maximise the active catalytic surface area at the Cathode Catalyst Layer (CCL) to increase the available reaction area of the stack and to avoid starvation at the catalyst sites. The PEMFC stack model includes a spatial discretisation that permits the control strategy to take into account the internal conditions of the system. These internal states are estimated and fed to the NMPC via a Nonlinear Distributed Parameters Observer (NDPO). The air-fed cathode of the PEMFC simulation model includes a two-phase water model for better representation of the stack voltage. The stack temperature is regulated through the use of an active cooling system. The control strategy is evaluated in an automotive application using a driving cycle based on the New European Driving Cycle (NEDC) profile as the case study.
control nonlinearities, observability, power system control, predictive control.
Electrochemically active surface area, nonlinear model predictive control, nonlinear observation, proton exchange membrane fuel cells, degradation, starvation
J.A. Luna, E. Usai, A.P. Husar and M. Serra. Enhancing the efficiency and lifetime of a proton exchange membrane fuel cell using nonlinear model predictive control with nonlinear observation. IEEE Transactions on Industrial Electronics, 2017, to appear.