To ensure optimal performance and safety of AAM (Advanced Air Mobility) aircraft throughout their operational stages (takeoff, ascent, cruise, descent, and landing), an ABMS (Advanced Battery Management System) is required, featuring real-time battery state diagnosis technology that accounts for dynamic capacity changes.
[Global Partnership Project funded by Korea Institute for Advancement of Technology]
Investigating electric vertical take-off and landing (eVTOL) aircraft flight patterns and developing their power and energy consumption models is critical for advanced air mobility (AAM) and urban air mobility (UAM) domains. This new transportation concept can represent a promising solution to alleviate traffic congestion, reduce travel times, and lower carbon emissions in urban areas. However, their success hinges on accurately modeling flight patterns and energy consumption to ensure safety, efficiency, and reliability. By understanding flight patterns, researchers can optimize routing, airspace management, and flight trajectories, enhancing the scalability of eVTOL operations in complex urban environments.
Successful AAM and UAM applications depend on the accurate modeling of flight patterns and energy consumption to ensure safety, efficiency, and reliability. By understanding flight patterns, researchers can optimize flight trajectories and airspace management to enhance the scalability of eVTOL operations in complex urban environments. Especially, researchers need to identify key factors influencing energy efficiency and battery conditions, such as payload and weather conditions. In this section, we provide an overview of the trajectory planning and optimization for eVTOL aircraft. These considerations are critical to increase and improve operational efficiency, sustainability, safety, and scalability.
Publications:
To be posted
Energy consumption models for eVTOLs are critical, as they determine battery requirements, flight range, and overall operational feasibility. These models enable the design of efficient power management systems, ensuring eVTOLs can meet the stringent performance demands of frequent short-haul flights while maintaining safety margins. Moreover, they help in identifying key factors influencing energy efficiency, such as aerodynamic design, payload, and weather conditions, driving innovation in eVTOL technology. Ultimately, this research is pivotal in addressing the technical and regulatory challenges needed to make eVTOLs a viable and sustainable mode of transportation.
Publications:
To be posted.
Stanford University Aerospace Vehicle Environment (SUAVE) is an advanced aircraft design and optimization simulation tool developed by Aerospace Vehicle Design at Stanford University, which aims to support the analysis and simulation of eVTOL (i.e., electric vertical takeoff and landing) aircraft (https://arc.aiaa.org/doi/10.2514/6.2015-3087). This tool is based on the Python language with open-source packages (e.g, numpy, scipy, and scikit-learn) so that users can easily access and manipulate the functions in SUAVE based on their preferences. It provides a multi-disciplinary platform that integrates aerodynamics, propulsion, structures, energy storage, and mission performance modeling, which allows users to conduct a comprehensive evaluation of numerous types of eVTOL configurations, such as multi-rotor, vectored thrust, and lift+cruise. Given the flight profiles and eVTOL system parameters (e.g., wing span, number of rotors and propellers, and battery pack size), SUAVE provides the information for eVTOL performance, including energy consumption/usage and required power for performing the predefined flight profiles. By facilitating rapid prototyping and system-level analysis, SUAVE contributes to the advancement of next-generation electric aviation technologies.
See more: https://suave.stanford.edu/index.html
NASA Generic-Urban-Air-Mobility (GUAM) is designed to model and simulate the behavior of an UAM aircraft, specifically a generic Lift+Cruise vehicle configuration similar to NASA's own designs. The simulator can simulate various flight scenarios for UAM aircraft. It supports analyzing aircraft dynamics, control algorithms, trajectory planning, and actuator models for flight phases like hover, transition, and cruise. The simulator is developed in MATLAB/Simulink, with several functions and scripts control different aspects of the simulation. Two aerodynamics models are available in the simulator: a low-fidelity strip-theory model and a higher-fidelity polynomial model based on computational fluid dynamics (CFD). The user can choose between these models depending on the trade-off between fidelity and computational speed.
See more: https://github.com/nasa/Generic-Urban-Air-Mobility-GUAM
Figure sources:
Publications
To be posted.
People
Current
Sooyung Byeon, Post-doc researcher
Sounghwan Hwang, Ph.D. student
Guanlin Wu, Ph.D. student
Minhyoung Hong, undergraduate student
Past
To be posted
Korea Ministry of Trade, Industry and Energy
Korea Institute for Advancement of Technology
From 2024 (Q3) to 2027
This material is based upon work supported by the Korea Ministry of Trade, Industry and Energy. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Korea Ministry of Trade, Industry and Energy.