Nonlinear Model Predictive Control for the Energy Management of Fuel Cell Hybrid Electric Vehicles in Real Time

Abstract

This article proposes an energy management system (EMS) for a fuel cell (FC) hybrid electric vehicle. The EMS is based on nonlinear model predictive control (NMPC) and employs a recurrent neural network (RNN) for modeling a proton exchange membrane FC. The NMPC makes possible the formulation of control objectives not allowed by a linear model predictive control (MPC), such as maximum efficiency point tracking of the FC, while the RNN can accurately predict the FC nonlinear dynamics. The EMS was implemented on a low-cost development board, and the experiments were performed in real time on a hardware-in-the-loop test bench equipped with a real 3-kW FC stack. The experimental results demonstrate that the NMPC EMS is able to meet the vehicle's energy demand, as well as to operate the FC in its most efficient region. Moreover, a comparative study is performed between the proposed NMPC, a linear MPC, and hysteresis band control. The results of this comparative study demonstrate that the NMPC provides a better fuel economy and can reduce FC degradation.