Modelling and controlling of polymer electrolyte fuel cell systems

This thesis focuses on the modelling and controlling of polymer electrolyte fuel
cell (PEFC) systems. A system level dynamic PEFC model has been developed
to test the system performance (output voltage, reactants gas partial pressures,
and stack temperature) for different operating conditions. The simulation results
are in good agreement with the experimental data, which indicates that the
PEFC model is well qualified to capture the dynamic performance of the PEFC
system. Controlling strategies play a significant role in improving the fuel cell
system’s reliability. Novel model predictive control (MPC) controllers and proportional–integral–derivative (PID) controllers are proposed and implemented in
different PEFC systems to control voltage and regulate temperature to enhance
system performance. MPC controllers show superior performance to PID controllers in tracking the reference value, with less overshoot and faster response. A
novel hydrogen selective membrane reactor (MR) is designed for methanol steam
reforming (MSR) to produce fuel cell grade hydrogen for PEFC stack use. The
backpropagation (BP) neural network algorithm is applied to find the mapping
relation between the MR’s operating parameters and the PEFC system’s output
performance. Simulation results show that the BP neural network algorithm can
well predict the system behaviour and that the developed mapping relation model
can be used for practical operation guidance and future control applications.