Adaptive hierarchical energy management strategy for fuel cell/battery hybrid electric UAVs

Energy management is crucial to realize efficient operation for hybrid electric UAVs. Adaptive hierarchical energy management strategy (AHEMS) is proposed to improve energy management efficiency for hybrid electric UAVs. AHEMS includes an optimization layer and a tracking layer. The former optimizes battery state of charge (SOC) reference trajectory and demand power by considering hybrid energy system characteristics, flight mission requirement and UAV model constraints. Sequential convex optimization method is proposed to solve battery SOC global reference trajectory. The latter realizes online power allocation by tracking the battery SOC reference trajectory through proposed model predictive control based on convex optimization. Meanwhile, fuzzy logic neural network is proposed to adaptively adjust prediction horizon for balancing the computational cost and optimality. AHEMS takes battery health management into account and prevents battery from over charging or over discharging. Numerical simulation results demonstrate that AHEMS can improve the energy management efficiency. Less hydrogen is consumed and battery is used more efficiently. Compared with nonlinear model predictive control (NMPC) and fuzzy logic state machine (FLSM), AHEMS saves 19.2% and 29.7% hydrogen fuel, respectively. Experimental results indicate that AHEMS is applicable for online energy management. It has good energy management effect. AHEMS is conducive to promoting the application of hybrid electric UAVs.