Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells

Fuel cells are a promising solution for increasing driving range of electric vehicles. To guarantee the high efficiency and stable operation of fuel cells, the effective regulation of oxygen and hydrogen reactants is needed. During varied driving conditions, in which the drastic current demand changes may result in insufficient reactant, the fuel cell can even be damaged. In this paper, a hierarchical model predictive control (HMPC) strategy is proposed based on the deep learning for vehicle speed predictions. Speed variation predictions are considered by the MPC to regulate the air supply system and preventing oxygen starvation in the fuel cell stack. The problems of fuel cell oxygen stoichiometry control are, first, stated with the preliminary energy management description as well as with the limitations of the traditional MPC. The deep learning Back Propagation (BP) neural network was then designed as the first-level predictor to forecast the vehicle speed by training with integrated driving cycles, and predict the fuel cell current based on its cathode flow model. Subsequently, the second-level MPC used the current disturbance prediction and filling is introduced to regulate the oxygen mass flow. The simulation results for the MANHATTAN drive cycle demonstrated that the root mean square error (RMSE) for speed predictions was less than 1 km/h. Compared with the conventional MPC, HMPC offers better robustness in the face of influence from current changes induced by speed-variations, and the RMSE of the oxygen stoichiometry control was decreased by 63.37%.