Optimized Energy Management Strategy for Hybrid Fuel Cell Powered Drones in Persistent Missions Using Real Flight Test Data

This paper proposes an optimized energy management strategy (EMS) for hybrid electric fuel cell/battery-based drones focusing on fuel economy while extending sources lifespans in persistent missions. An off-the-shelf drone fed by a 650 W fuel cell is selected as a case study, where the power splitting is conventionally managed by a simple rule-based method. Then, a multi-objective genetic algorithm is used to optimize the proposed EMS parameters considering three scenarios regarding battery state of charge, namely charge sustaining, depleting, and increasing. There-fore, advantages of rule-based strategy and genetic algorithm are combined in an online EMS to fit on drone applications. Extensive simulation results demonstrate that the proposed approach allows power sources to operate within their rated area, prolonging their service life, and leading to 5.1% of fuel consumption reduction. Thus, the autonomy will he increased depending on the carried hydrogen quantity, and the world record can be extended by about 37 min. It may also lead to benefit in the operating cost achieving 1450(sic) during one fuel cell stack lifecycle. In fleet tasks, the benefit can be further multiplied.

Automated summary

This paper proposes an optimized energy management strategy for hybrid electric fuel cell/battery-based drones, aiming to improve fuel economy and extend the lifespan of energy sources. By combining rule-based strategy and genetic algorithm, an online energy management system is developed for drone applications. Simulation results demonstrate that the proposed approach allows power sources to operate within their rated area, prolonging their service life, and leading to a reduction in fuel consumption. These findings suggest that the proposed strategy can enhance the autonomy and achieve cost benefits in operating fuel cell stack during its lifecycle.