SpringerLink
Log in

Data-Driven Urban Air Mobility Flight Energy Consumption Prediction and Risk Assessment

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 824))

Included in the following conference series:

  • 189 Accesses

Abstract

The current technological advancements revolutionizing the concept of Urban Air Mobility (UAM), has a concurrent need to quantify the operational safety of these vehicles in terms of their associated risk. Providing safety certification of flight operations of UAM vehicles is critical as the concept relies on battery powered electrically Vertical Takeoff and Landing (eVTOL) vehicles, to operate in the current Air traffic control. In this paper, a data-driven method for UAM vehicle energy consumption prediction and risk quantification with conditional value-at-risk based on energy consumption distribution is presented. Significant factors affecting energy consumption, such as density altitude, aircraft design, airspeed, and collision avoidance algorithms, are considered in the data-driven based energy consumption prediction of multiple eVTOL flights. Additionally, a risk metric was deployed to evaluate the risk associated with worst case energy dissipating flights. Our result shows that the proposed approach provides a generalized method to quantify operational safety of UAM network over a given region.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bacchini, A., Cestino, E.: Electric vtol configurations comparison. Aerospace 6(3), 26 (2019)

    Article  Google Scholar 

  2. Brownlee, J.: Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery (2016)

    Google Scholar 

  3. Bulusu, V., Sengupta, R., Mueller, E.R., Min Xue, A.: Throughput based capacity metric for low-altitude airspace. In: Aviation Technology. Integration, and Operations Conference, p. 3032 (2018)

    Google Scholar 

  4. Choudhry, A., Moon, B., Patrikar, J., Samaras, C., Scherer, S.: Cvar-based flight energy risk assessment for multirotor uavs using a deep energy model. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 262–268. IEEE (2021)

    Google Scholar 

  5. Clarke, M., Smart, J., Botero, E.M., Maier, W., Alonso, J.J.: Strategies for posing a well-defined problem for urban air mobility vehicles. In: AIAA Scitech 2019 Forum, p. 0818 (2019)

    Google Scholar 

  6. Elevate, U.: Uber air vehicle requirements and missions. Technical Report, Uber (2018)

    Google Scholar 

  7. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Google Scholar 

  8. Hill, B.P., DeCarme, D., Metcalfe, M., Griffin, C., Wiggins, S., Metts, C., Bastedo, B., Patterson, M.D., Mendonca, N.L.: Uam vision concept of operations (conops) uam maturity level (uml) (2020)

    Google Scholar 

  9. www.nari.arc.nasa.gov/sites/default/files/attachments/UAMS ConOps v1.0.pdf. Concept of operations v1.0. (2020)

  10. www.nodis3.gsfc.nasa.gov/displayDir.cfm?t=NPR&c=7900&s=3D Nasa procedural requirements for aircraft operations management npr 7900.3d, chapter 2: Airworthiness and maintenance. 2017–2023

  11. Jabr, R.A.: Robust self-scheduling under price uncertainty using conditional value-at-risk. IEEE Trans. Power Syst. 20(4), 1852–1858 (2005)

    Google Scholar 

  12. Jang, D.-S., Ippolito, C.A., Sankararaman, S., Stepanyan, V.: Concepts of airspace structures and system analysis for uas traffic flows for urban areas. In: AIAA Information Systems-AIAA Infotech@ Aerospace, p. 0449 (2017)

    Google Scholar 

  13. Johnson, M., Jung, J., Rios, J., Mercer, J., Homola, J., Prevot, T., Mulfinger, D., Kopardekar, P.: Flight test evaluation of an unmanned aircraft system traffic management (utm) concept for multiple beyond-visual-line-of-sight operations. In: USA/Europe Air Traffic Management Research and Development Seminar (ATM2017), number ARC-E-DAA-TN39084 (2017)

    Google Scholar 

  14. Joulia, A., Dubot, T., Bedouet, J.: Towards a 4d traffic management of small uas operating at very low level. In: ICAS, 30th Congress of the International Council of the Aeronautical Sciences (2016)

    Google Scholar 

  15. Lukaczyk, T.W., Wendorff, A.D., Colonno, M., Economon, T.D., Alonso, J.J., Orra, T.H., Ilario, C.: Suave: an open-source environment for multi-fidelity conceptual vehicle design. In: 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, p. 3087 (2015)

    Google Scholar 

  16. Maas, J., Sunil, E., Ellerbroek, J., Hoekstra, J.: The effect of swarming on a voltage potential-based conflict resolution algorithm. In: Submitted to the 7th International Conference on Research in Air Transportation (2016)

    Google Scholar 

  17. Melo, S.P., Cerdas, F., Barke, A., Thies, C.,Spengler, T.S., Herrmann, C.: Life cycle engineering of future aircraft systems: the case of evtol vehicles. Procedia CIRP 90, 297–302 (2020)

    Google Scholar 

  18. Peinecke, N., Kuenz, A.: Deconflicting the urban drone airspace. In: 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), pp. 1–6. IEEE (2017)

    Google Scholar 

  19. Phillips, M., Likhachev, M.: Sipp: safe interval path planning for dynamic environments. In: 2011 IEEE International Conference on Robotics and Automation, pp. 5628–5635. IEEE (2011)

    Google Scholar 

  20. Ramee, C., Mavris, D.N.: Development of a framework to compare low-altitude unmanned air traffic management systems. In: AIAA Scitech 2021 Forum, p. 0812 (2021)

    Google Scholar 

  21. R Tyrrell Rockafellar, Stanislav Uryasev, et al. Optimization of conditional value-at-risk. Journal of risk, 2:21–42, 2000

    Google Scholar 

  22. Russell, S., Norvig, P.: A* search: minimizing the total estimated solution cost. Artif. Intell. 94–99 (2010)

    Google Scholar 

  23. Sachs, P., Dienes, C., Dienes, E., Egorov, M.: Effectiveness of preflight deconfliction in high-density uas operations. Technical Report, Altiscope, Technical report (2018)

    Google Scholar 

  24. Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdiscip. Rev.: Data Mining Knowl. Discov. 8(4), e1249 (2018)

    Google Scholar 

  25. Sarkar, M., Yan, X., Gebru, B., Nuhu, A.-R., Gupta, K.D., Vamvoudakis, K.G., Homaifar, A.: A data-driven approach for performance evaluation of autonomous evtols (2022)

    Google Scholar 

  26. Sarkar, M., Yan, X., Girma, A., Homaifar, A.: A framework for evtol performance evaluation in urban air mobility realm (2021). ar**v:2111.05413

  27. Sedov, L., Polishchuk, V.: Centralized and distributed utm in layered airspace. In: 8th International Conference on Research in Air Transportation, pp. 1–8 (2018)

    Google Scholar 

  28. Sunil, E., Hoekstra, J., Ellerbroek, J., Bussink, F., Vidosavljevic, A., Delahaye, D., Aalmoes, R.: The influence of traffic structure on airspace capacity. In: 7th International Conference on Research in Air Transportation (2016)

    Google Scholar 

  29. Thibbotuwawa, A., Nielsen, P., Zbigniew, B., Bocewicz, G.: Energy consumption in unmanned aerial vehicles: a review of energy consumption models and their relation to the uav routing. In: International Conference on Information Systems Architecture and Technology, pp. 173–184. Springer (2018)

    Google Scholar 

  30. Thompson, E.L., Taye, A.G., Guo, W., Wei, P., Quinones, M., Ahmed, I., Biswas, G., Quattrociocchi, J., Carr, S., Topcu, U., et al.: A survey of evtol aircraft and aam operation hazards. In: AIAA AVIATION 2022 Forum, p. 3539 (2022)

    Google Scholar 

  31. Yang, X.-G., Liu, T., Ge, S., Rountree, E., Wang, C.-Y.: Challenges and key requirements of batteries for electric vertical takeoff and landing aircraft. Joule 5(7), 1644–1659 (2021)

    Article  Google Scholar 

  32. Zhu, G., Wei, P.: Low-altitude uas traffic coordination with dynamic geofencing. In: 16th AIAA Aviation Technology, Integration, and Operations Conference, p. 3453 (2016)

    Google Scholar 

Download references

Acknowledgment

This research work is sponsored by the National Aeronautics and Space Administration University Leadership Initiative (NASA-ULI 2019) research grant number 80NSSC20M0161. The authors would like to thank Mr. Frank Aguilera who has provided us constructive feedbacks.

Author information

Authors and Affiliations

  1. North Carolina A &T State University, Greensboro, NC, 27411, USA

    Yonas Ayalew, Wendwosen Bedada & Abdollah Homaifar

  2. NASA Ames Research Center, Mountain View, CA, 94035, USA

    Kenneth Freeman

Authors
  1. Yonas Ayalew
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wendwosen Bedada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdollah Homaifar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenneth Freeman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdollah Homaifar .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayalew, Y., Bedada, W., Homaifar, A., Freeman, K. (2024). Data-Driven Urban Air Mobility Flight Energy Consumption Prediction and Risk Assessment. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-031-47715-7_24

Download citation

Publish with us

Policies and ethics

Search

Navigation