Design of Genetic Programming Control Algorithm for Low-Temperature PEM Fuel Cell

Abstract

An effective control system for the air supply in fuel cell systems (FCS) is required to prevent oxygen starvation and to maximize the net power. For this purpose, conventional feedback and adaptive controllers are designed using genetic programming (GP). To minimize the time required for the GP convergence, FCS models of different complexity are studied and a comparison between them is carried out. Guidelines on applying the GP approach based on data obtained from simulations are developed along with a specially designed cost function that promotes closed-loop linearization. The advantage of this design method lies in its applicability to complex nonlinear systems for which nonlinear control methods may not be applicable. Adaptation is added to the oxygen excess ratio (OER) regulation problem by training a neural network that provides the optimal OER reference based on the stack current and temperature. The performance of both the regulation and adaptive controllers is tested under noise in the compressor flow and the stack current measurements. The robustness of the GP controllers is observed through the frequency response analysis.