  The global trend toward renewable power generation has drawn great attention
to hydrogen Fuel Cells (FCs), which have a wide variety of applications, from
utility power stations to laptops. The Multi-stack Fuel Cell System (MFCS),
which is an assembly of FC stacks, can be a remedy for obstacles in high-power
applications. However, the output voltage of FC stacks varies dramatically
under variable load conditions; hence, in order for MFCS to be efficiently
operated and guarantee an appropriate load-current sharing among the FC stacks,
advanced converter controllers for power conditioning need to be designed. An
accurate circuit model is essential for controller design, which accounts for
the fact that the parameters of some converter components may change due to
aging and repetitive stress in long-term operations. Existing control
frameworks and parametric and non-parametric system identification techniques
do not consider the aforementioned challenges. Thus, this paper investigates
the potential of a data-driven method that, without system identification,
directly implements control on paralleled converters using raw data. Based on
pre-collected input/output trajectories, a non-parametric representation of the
overall circuit is produced for implementing predictive control. While
approaching equal current sharing within the MFCS, the proposed method
considers the minimization of load-following error and mitigation of
circulating current between the converters. Simulation results verify the
effectiveness of the proposed method.
