In this study the potential for fuel savings through electric taxiing is investigated. Therefore simple models are used to investigate the difference of fuel consumption during the ground phase and the flight phase of a gate-to-gate mission. By using these models the fuel savings can be estimated, which are the sum of the fuel saved during ground operations and the additional fuel required during the flight phase due to an increased Operating Empty Weight. The models are applied to actual data of flights conducted by domestic carriers within the United States National Airspace System. Results show that electric taxiing offers the potential for fuel savings depending on the flight mission, i.e. the ratio of time an aircraft spends on ground and the flight distance. A parametric analysis is conducted to investigate the sensitivity of the results for different constraints. The study concludes with a comparison of the concept compared to other operational or technological measures aiming to reduce fuel consumption on ground. NomenclatureATU = Autonomous Taxiing Unit APU = Auxiliary Power Unit BTS = Bureau of Transportation Statistics DOC = Direct Operating Costs ESUT = Engine Start Up Time ECDT = Engine Cool Down Time F Blockfuel = Fuel for a flight mission F Ground = Fuel for ground operations F Airborne = Tripfuel FF Idle = Fuel flow of the main engines in idle mode FF APU = Fuel flow of the APU FAA = Federal Aviation Administration FCOM = Flight Crew Operating Manual ICAO = International Civil Aviation Organisation ISA = International Standard Atmosphere n eng = Number of engines NAS = National Airspace System NO x = Nitrogene Oxide OEW = Operating Empty Weight t Ground = Ground Time t Taxi-in = Taxi-in Time t Taxi-out = Taxi-out Time

The current push in automation, communication, and electrical energy storage technologies has the potential to lift urban mobility into the sky. As several urban air mobility (UAM) concepts are conceivable, all relevant physical effects as well as mutual interrelations of the UAM system have to be addressed and evaluated at a sufficient level of fidelity before implementation. Therefore, a collaborative system of systems modeling approach for UAM is presented. To quickly identify physical effects and cross-disciplinary influences of UAM, a pool of low-fidelity physical analysis components is developed and integrated into the Remote Component Environment (RCE) workflow engine. This includes, i. a., the disciplines of demand forecast, trajectory, vertiport, and cost modeling as well as air traffic flow and capacity management. The definition and clarification of technical interfaces require intensive cooperation between specialists with different areas of expertise. To reduce this communication effort, the Common Parametric Aircraft Configuration Schema (CPACS) is adapted and used as central data exchange format. The UAM system module is initially applied for a 24-hour simulation of three generic networks in Hamburg City. After understanding the basic system-level behavior, higher level analysis components and feedback loops must be integrated in the UAM system module for evaluation and optimization of explicit operating concepts.

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