Model predictive control energy management strategy of fuel cell hybrid electric vehicle

Abstract

Model predictive control (MPC) based energy management strategies (EMS) are promising to achieve high-efficiency power conversion for different hybrid electric vehicles. In this work, we investigate the impact of velocity prediction on the performance of EMS. For this, MPC controllers are designed respectively for fuel cell hybrid electric vehicles (FCHEV) using four prediction settings: Prescient MPC, Frozen time MPC, exponentially decreasing MPC, and MPC with Markov chain model. The comparison of the results using different driving cycles is performed to study the effects of prediction horizon and prediction accuracy on the performance of EMS, in terms of hydrogen consumption and battery charge sustainability. Simulation results show that the performance of MPC-based EMS is highly dependent on the prediction accuracy and the control horizon length. With proper velocity prediction methods and horizon length configurations, low hydrogen consumption and sustainable battery charge can be achieved. Moreover, the necessity of co-designing the prediction model and the horizon length by specifying the driving condition is highlighted.