An intelligent power management system for unmanned earial vehicle propulsion applications

摘要

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi-nent aviation concept due to the advantageous such as stealth operation andzero emission. In addition, fuel cell powered electric UAVs are more attrac-tive as a result of the long endurance capability of the propulsion system.This dissertation investigates novel power management architecture for fuelcell and battery powered unmanned aerial vehicle propulsion application.The research work focused on the development of a power managementsystem to control the hybrid electric propulsion system whilst optimizingthe fuel cell air supplying system performances.The multiple power sources hybridization is a control challenge associatedwith the power management decisions and their implementation in the powerelectronic interface. In most applications, the propulsion power distribu-tion is controlled by using the regulated power converting devices such asunidirectional and bidirectional converters. The amount of power sharedwith the each power source is depended on the power and energy capacitiesof the device. In this research, a power management system is developedfor polymer exchange membrane fuel cell and Lithium-Ion battery basedhybrid electric propulsion system for an UAV propulsion application. Ini-tially, the UAV propulsion power requirements during the take-off, climb,endurance, cruising and maximum velocity are determined. A power man-agement algorithm is developed based on the UAV propulsion power re-quirement and the battery power capacity. Three power states are intro-duced in the power management system called Start-up power state, Highpower state and Charging power state. The each power state consists ofthe power management sequences to distribute the load power between thebattery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi-nent aviation concept due to the advantageous such as stealth operation andzero emission. In addition, fuel cell powered electric UAVs are more attrac-tive as a result of the long endurance capability of the propulsion system.This dissertation investigates novel power management architecture for fuelcell and battery powered unmanned aerial vehicle propulsion application.The research work focused on the development of a power managementsystem to control the hybrid electric propulsion system whilst optimizingthe fuel cell air supplying system performances.The multiple power sources hybridization is a control challenge associatedwith the power management decisions and their implementation in the powerelectronic interface. In most applications, the propulsion power distribu-tion is controlled by using the regulated power converting devices such asunidirectional and bidirectional converters. The amount of power sharedwith the each power source is depended on the power and energy capacitiesof the device. In this research, a power management system is developedfor polymer exchange membrane fuel cell and Lithium-Ion battery basedhybrid electric propulsion system for an UAV propulsion application. Ini-tially, the UAV propulsion power requirements during the take-off, climb,endurance, cruising and maximum velocity are determined. A power man-agement algorithm is developed based on the UAV propulsion power re-quirement and the battery power capacity. Three power states are intro-duced in the power management system called Start-up power state, Highpower state and Charging power state. The each power state consists ofthe power management sequences to distribute the load power between thebattery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate thefuel cell system and the battery into the propulsion motor drive. The mainobjective of the power management system is to obtain the controlled fuelcell current profile as a performance variable. The relationship between thefuel cell current and the fuel cell air supplying system compressor poweris investigated and a referenced model is developed to obtain the optimumcompressor power as a function of the fuel cell current. An adaptive controlleris introduced to optimize the fuel cell air supplying system performancesbased on the referenced model. The adaptive neuro-fuzzy inferencesystem based controller dynamically adapts the actual compressor operatingpower into the optimum value defined in the reference model. The onlinelearning and training capabilities of the adaptive controller identify thenonlinear variations of the fuel cell current and generate a control signal forthe compressor motor voltage to optimize the fuel cell air supplying systemperformances.The hybrid electric power system and the power management system weredeveloped in real time environment and practical tests were conducted tovalidate the simulation results.