Health Monitoring and Prognostics for More Electric Aircrafts

Goebel, Kai ; Gorospe, George ; Kulkarni, Chetan ; Schumann, Johann ; Cuong Chi, Quach 'Patrick' ; Hogge, Edward

关键词: ELECTRIC BATTERIES;    ELECTRIC MOTOR VEHICLES;    FLIGHT OPERATIONS;    FLY BY WIRE CONTROL;    HEALTH;    PILOTLESS AIRCRAFT;    AIRSPACE;    DECISION MAKING;    FLIGHT CONDITIONS;    MANEUVERS;   

RP-ID  :  ARC-E-DAA-TN60957

学科分类：航空航天科学

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining flying time/distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of battery charge while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem.For electric aircraft, propulsion is based on power generated from batteries. Thus, it is critical to monitor battery state charge and to estimate the ability of the battery to support mission activities as it is being discharged during flight operation. The ability of the vehicle to complete its given mission very much depends on the charge left in the batteries based on its operational route, maneuvering, weather conditions along with aging health of the batteries. Hence, for the purpose this discussion, consider the scenario of an unmanned electric aircraft that has some planned sequence of waypoints to reach throughout its mission. In such a scenario, for this particular aircraft, and within the region it is flown, at most two minutes are required to safely land the aircraft. Thus, it is desired to predict at which point in time the aircraft must begin to head to the runway and land.