Real-Time Model Predictive Energy Management for Portable Air-Cooled Fuel Cell/Lithium-Ion Battery Hybrid Power Systems

This study investigates real-time model predictive energy management for portable air-cooled fuel cell/lithium-ion battery hybrid power systems. To capture the coupled electrical and thermal behavior of the system while maintaining computational efficiency for online control, a control-oriented lumped-parameter model is developed. The model describes the fuel cell voltage characteristics and thermal dynamics, the lithium-ion battery state of charge (SOC), and the level of hydrogen (LOH) in the hydrogen tank. Based on this model, a model predictive control (MPC)-based energy management strategy is proposed to coordinate the power distribution between the fuel cell and lithium-ion battery subject to power, ramp-rate, SOC, and LOH constraints. The proposed strategy is compared with a conventional rule-based strategy under rated-power, short-duration load variation, and long-duration load variation conditions. Simulation results show that the proposed strategy smooths fuel cell power, maintains the battery SOC within a reasonable range, and improves coordinated energy utilization. Hardware-in-the-loop experiments using STM32 controllers further verify its real-time feasibility. Compared with the rule-based strategy, the proposed method reduces the maximum fuel cell power variation rate by 85.7% in the HIL test, demonstrating improved fuel cell power stability.