An explicit predictive controller for fuel-cell electric vehicles incorporating the hierarchical architecture

In light of the severity of global climate change and the energy crisis, fuel cell electric vehicles (FCEVs) have garnered significant attention as a potential solution. Integrating advanced energy management strategies (EMSs) into FCEVs can effectively optimize the energy consumption performance of the highly nonlinear powertrain across various driving conditions. In this paper, a hierarchical explicit model predictive control energy management strategy (H-EMPC EMS) is proposed to enhance the economic performance of FCEVs and ensure control robustness across diverse driving conditions. Firstly, a hierarchical control architecture is developed to improve the adaptability of the EMS to diverse driving conditions. The upper layer performs driving conditions recognition and operation modes switching by observing the vehicle state. The lower layer determines power distribution throughout the powertrain. Secondly, an explicit MPC (eMPC) controller is developed to make the powertrain promptly respond to the power demand for the vehicle. To bolster the real-time application capability of the controller, systematic optimization is carried out by combining eMPC offline control law generation and online control law invocation. Thirdly, a feedback compensator is integrated into the controller to mitigate the effects of parameter variations on control and strengthen the robustness of the EMS. Finally, simulation evaluations and hardware-in-the-loop (HIL) tests demonstrate that the proposed H-EMPC EMS can improve economic performance and guarantee real-time performance for the studied FCEV. Compared with other baselines, the energy-saving capability is remarkable, showcasing its promising performance.