Experimental Analysis of a Fuzzy Scheme against a Robust Controller for a Proton Exchange Membrane Fuel Cell System

Abstract

Proton exchange membrane fuel cells (PEMFC) are capable of transforming chemical energy into electrical energy with zero emissions. Therefore, these devices had been a point of attention for the scientific community as to provide another solution to renewable sources of energy. Since the PEMFC is commonly driven with a power converter, a controller has to be implemented to supply a convenient voltage. This is an important task as it allows the system to be driven at an operative point, which can be related to the maximum power or an user desired spot. Along this research article, a robust controller was compared against a fuzzy logic strategy (with symmetric membership functions) where both were implemented to a commercial PEMFC through a dSPACE 1102 control board. Both proposals were analysed in an experimental test bench. Outcomes showed the advantages and disadvantages of each scheme in chattering reduction, accuracy, and convergence speed.

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

“…Additionally, the following symbols are used: N 0 is the number of cells in series in the stack, E 0 is the ideal standard potential, R 0 is the universal gas constant, T 0 is the absolute temperature and F 0 is the Faraday's constant. Finally, activation losses and ohmic losses are described by Equations ( 8) and (9), respectively, where R int is the internal resistance while B and C are activation voltage constants. The values of model parameters are given in Table 1.…”

“…Different control strategies are possible for optimising the power split between the battery and PEM fuel cell to maximise system efficiency and reduce fuel consumption [8]. Simple controllers, including a Sliding-Mode Controller (SMC) and a fuzzy logic controller, can supply a convenient voltage [9]. A more advanced approach is to use a fuzzy Proportional-Integral-Derivative (PID) controller [10] for temperature control of the fuel cell cooling system or an adaptive SMC algorithm which is integrated with the radial basis function neural network to control the current or voltage of the fuel cell power supply [11].…”