Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle

Abstract

Proton exchange membrane fuel cell (PEMFC) hybrid vehicles offer a long driving range but are heavily dependent on energy management strategies (EMS). Traditional EMS methods, such as rule-based approaches and optimization-based methods like model predictive control (MPC), either lack flexibility or are computationally complex and rely on prior driving experience. To overcome these limitations, this study proposes a semi-empirical approach that combines state machine (SM) and MPC in a novel hybrid EMS (SM-MPC) to optimize power distribution in a 100 kW PEMFC hybrid vehicle. The SM-MPC strategy uses SM to handle fast power fluctuations and MPC to manage slow variations, balancing real-time adaptability and efficiency. Simulation results based on the NEDC and HWFET driving cycles show that compared to the traditional MPC method, SM-MPC significantly reduces hydrogen consumption by 7.11 g (NEDC) and 1.89 g (HWFET). Additionally, the proposed method effectively maintains the state of charge (SOC) of the lithium-ion battery using a PID controller and ensures the PEMFC stack temperature remains within ±5.8 °C. Overall, the SM-MPC strategy improves energy efficiency, reduces fuel consumption, and enhances the stability of the hybrid power system, offering a promising solution for real-time energy optimization in fuel cell vehicles.