From Strategic Planning to Tactical Adjustments: An eVTOL Trajectory Management Framework for Urban Air Mobility
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Kim, Seulki
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Abstract
Urban Air Mobility (UAM) is emerging as a revolutionary transportation concept aimed at alleviating ground traffic congestion in densely populated urban areas. By utilizing electric Vertical Takeoff and Landing (eVTOL) aircraft, UAM promises to provide rapid, efficient, and environmentally friendly air transportation for short to medium distances within and between cities. The increasing urbanization of the global population, coupled with advancements in electric propulsion, battery technology, and automation, has motivated significant interest in UAM as a potential solution to ground transportation challenges.
However, the realization of UAM operations faces several critical barriers. These include the need for comprehensive airspace management, the development of reliable aircraft, and integration with existing urban infrastructure and communities. Particularly, the motivation for this research stems from the unique operational complexities associated with managing multiple eVTOL flights in complex urban environments. These aircraft are anticipated to operate at low heights in obstacle-rich settings, relying on limited battery energy storage and subject to specific electrical and thermal powertrain constraints. The vertical takeoff and landing capabilities, while advantageous for urban operations, also impose significant power demands and rapidly deplete energy reserves. Additionally, the current technological limitations in battery energy density and their non-linear discharge behavior present challenges in energy management and flight planning.
Currently, trajectory management for conventional aircraft is primarily handled through a combination of pre-flight planning by airline dispatchers and real-time adjustments by pilots and air traffic controllers. Given the novel operational characteristics of UAM, however, traditional human-driven trajectory management systems may not suffice for the anticipated high traffic densities, rapid-paced operations envisioned for UAM, where a numerous number of aircraft will need to navigate through constrained urban airspace while adhering to strict energy and safety constraints. Moreover, the need for rapid decision-making in dynamic urban environments, especially during contingencies such as en-route diversions, necessitates more agile and automated management approaches. Consequently, these challenges underscore the necessity for an advanced automated system capable of ensuring safe, energy-efficient, and scalable management of eVTOL trajectories.
In response, this dissertation addresses these challenges through the development of an automated trajectory management framework for eVTOL UAM aircraft. The framework is designed to enable safe, energy-efficient trajectory generation and dynamic adjustments in response to evolving flight circumstances. Specifically, it consists of two primary components: a strategic planner and a tactical planner.
The strategic planner, developed using mixed-integer linear programming (MILP), generates pre-departure trajectories that optimize battery energy efficiency and collision avoidance, taking into account intricate powertrain constraints. This component successfully integrates operational constraints specific to manned eVTOL fights into the MILP formulation, allowing for the generation of trajectories that adhere to aviation regulations and standard flight procedures. Additionally, the development of a battery discharge model and thermal prediction capability enables to generate battery energy-efficient trajectories for multiple aircraft with the prediction capability of electrical and thermal profiles of powertrain along trajectories. Notably, this prediction of electrical and thermal behaviors along the trajectory help enable accurate pre-departure safety assessments to prevent potential energy deficits and powertrain issues during flights.
The tactical planner, built upon a Receding Horizon MILP (RH-MILP) approach, provides real-time trajectory adjustments and manages in-flight contingencies. This component demonstrates rapid computational efficiency, with trajectory recalculations completed in less than a second, allowing for dynamic adaptation to changing flight conditions. Furthermore, the creation of a diversion decision-making and planning tool aids pilots in selecting optimal alternate landing sites during contingencies, considering real-time aircraft and powertrain states.
Complementing the strategic planner, the tactical planner facilitates real-time adjustments to planned trajectories in response to evolving flight conditions and unforeseen contingencies. It employs a Receding Horizon MILP (RH-MILP), which allows for dynamic trajectory modifications while maintaining computational efficiency. Experiments demonstrate that the RH-MILP can generate real-time, energy-efficient trajectories for multiple eVTOL aircraft within a fraction of a second, significantly outperforming standalone MILP in terms of computational time. In addition, the tactical planner incorporates a diversion decision-making and planning tool that helps pilots manage in-flight contingencies by automatically selecting the best alternate landing sites and continuously adjusting the trajectory until safe diversion is completed. Several case studies demonstrate the tool's effectiveness in managing contingency scenarios, such as unexpected vertiport closures and partial battery pack disconnections.
The integration of these components forms a trajectory management framework capable of effectively managing UAM trajectories under both regular and irregular operational scenarios. To validate the proposed framework, several real-world use cases were simulated in the Southern California region, chosen for its unique geographical and urban characteristics. These use cases demonstrated the framework's capability in generating conflict-free trajectories through complex terrains and urban environments. One case study showcased the optimization of trajectories for a flight, navigating mountainous terrain while adhering to airspace regulations and powertrain constraints. Another case study simulated high-density UAM operations in Los Angeles city, optimizing trajectories for 50 aircraft departing or arriving at 10 vertiports within a 30-minute period. These simulations validated the framework's scalability and its ability to handle diverse operational scenarios, including multi-hop missions and in-flight contingencies such as unexpected vertiport closures and partial battery pack disconnections.
Ultimately, this automated trajectory management framework is expected to offer considerable benefits to UAM stakeholders, including pilots, operators, and air traffic controllers. UAM operators could leverage this framework to optimize flight planning and improve operational efficiency, pilots is able to receive enhanced decision support, particularly during off-nominal situations, and finally air traffic controllers could more effectively manage high-density urban air traffic. In conclusion, this research contributes to the advancement of UAM by addressing critical challenges in trajectory management. The developed framework demonstrates the potential for safe, efficient, and scalable eVTOL operations in complex urban environments, potentially accelerating the integration of this innovative transportation mode into urban airspace.
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Date
2024-09-05
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Dissertation