Artificial neural network-based model predictive control for optimal operating conditions in proton exchange membrane fuel cells

In the large-scale commercialization of proton exchange membrane fuel cells (PEMFC), efficient control of the dynamic operation requires the consideration of complex nonlinearities in current, temperature, pressure, and membrane hydration. In this study, an artificial neural network (ANN) model was developed and integrated with model predictive control (MPC) to provide optimal conditions for operating the PEMFC system under different current load changes. For this purpose, after modeling a 1.2 kW PEMFC in a MATLAB/Simulink environment, the accuracy of the model was validated within a maximum relative error of 2.30% compared to the experimental data. Next, the power production, temperature, and internal relative humidity of the model were extracted under all feasible operating conditions. The extracted data were used to train the nonlinear autoregressive network with exogenous inputs (NARX), and hyperparameters of neural networks were selected through Bayesian optimization. The NARX model trained with optimal parameters had a mean square error (MSE) of 2.14 × 10⁻⁴ and was integrated into the MPC to obtain neural network model predictive control (NNMPC). The developed NNMPC provides the optimal operating temperature and pressure for the PEMFC model at 10-s intervals through iterative simulation of the NARX under Bayesian optimization. To verify the performance of the control system, the developed NNMPC and fixed-setpoint scenario were compared. Each control scenario consisted of three current load profiles: low, medium, and high current. The NNMPC yielded improved system power by maintaining the optimal stack temperature, cathode pressure and membrane hydration for current changes at a low computational cost. The optimized power of the PEMFC system showed an average increase of 10.9% compared to fixed-setpoint condition.