Neural Generalized Predictive Control for Tracking Maximum Efficiency and Maximum Power Points of PEM Fuel Cell Stacks

This work proposes a Neural Generalized Predictive Control (NGPC) for tracking the maximum efficiency or the maximum power point of proton exchange membrane fuel cell (PEMFC) stacks. The NGPC consists of a nonlinear type of model predictive control (MPC) in which the prediction model is a neural network (NN). Such control strategy combines the advantages of PEMFC modeling using NNs and the MPC family of controllers. NNs are capable of modeling the whole nonlinear dynamics and the time-varying behavior of PEMFC stacks, while the MPC can easily handle system constraints and it manipulates multiple control variables of complex systems. Through its optimization algorithm, the NGPC determines an optimal current necessary for the stack to operate at maximum efficiency or maximum power. This paper demonstrates the operation of the proposed control technique through a simulation study of a grid-connected PEMFC power generation system.