Verification of Prognostic Algorithms to Predict Remaining Flying Time for Electric Unmanned Vehicles

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 19, 2020
Edward F. Hogge Brian M. Bole Sixto L. Vazquez Chetan S. Kulkarni Thomas H. Strom Boyd L. Hill Kyle M. Smalling Cuong C. Quach

Abstract

This paper addresses the problem of building trust in the online prediction of a eUAV’s remaining available flying time powered by lithium-ion polymer batteries. A series of ground tests are described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected during flight. The eUAV is repeatedly operated until the charge stored in powertrain batteries falls below a specified limit threshold. The time at which the limit threshold on battery charge is crossed is then used to measure the accuracy of the remaining flying time prediction. In our earlier work battery aging was not included. In this work we take into account aging of the batteries where the parameters were updated to make predictions. Accuracy requirements are considered for an alarm that warns operators when remaining flying time is estimated to fall below the specified limit threshold.

Abstract 36 | PDF Downloads 25

##plugins.themes.bootstrap3.article.details##

Keywords

fault detection, verification and validation, Aircraft Avionics, battery degradation, Robustness

References
Bole, B., Daigle, M., Gorospe, G.,. (2014). Online prediction of battery discharge and estimation of parasitic loads for an electric aircraft. European Conference of the Prognostics and Health Management Society.
Bole, B., Teubert, C., Quach, C., Hogge, E., Vazquez, S., Goebel, K., & Vachtsevanos, G. (2013). SIL/HIL replication of electric aircraft powertrain dynamics and inner-loop control for V&V of system health management routines. Annual Conference of the Prognostics and Health Management Society.
Ceraolo, M. (2000, November). New dynamical models of lead-acid batteries. IEEE Transactions of Power Systems, 15(4), 1184-1190.
Chen, M., & Rincon-Mora, G.A. (2006, June). Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion. 21(2), 504-511.
Dai, H., Wei, X., & Sun, Z. (2006). Online SOC estimation of high power lithium-ion batteries used on HEVs. In IEEE international conference on vehicular electronics and safety.
Daigle, M., Goebel, K. (2010). Improving computational efficiency of prediction in model-based prognostics using the unscented transform. Annual Conference of the Prognostics and Health Management Society, 2010.
Daigle, M., Kulkarni, C. (2013). Electrochemistry-based battery modeling for prognostics. Annual Conference of the Prognostics and Health Management Society, 2013.
Daigle, M., Saxena A. & Goebel, K. (2012). An efficient deterministic approach to model-based prediction uncertainty estimation. Annual Conference of the Prognostics and Health Management Society, 2012.
Ely, J., Koppen, S., Nguyen, T., Dudley, K., Szatkowski, G., Quach, C., Vazquez, S., Mielnik, J., Hogge, E., Hill, B. & Strom, T. (2011). Radiated Emissions From a Remote-Controlled Airplane - Measured in a Reverberation Chamber. NASA/TM-2011-217146.
Hoaglin, D. C., Mosteller, F., Tukey, J. W. (1983). Analysis of two-way tables by medians. In Emerson, J. & Hoaglin, D. (Eds.), Understanding Robust and Exploratory Data Analysis (176-182).
Hogge, E., Bole, B., Vazquez, S., Celaya, J., Strom, T., Hill, B., Smalling, K. & Quach, C. (2015). Verification of a remaining flying time prediction system for small electric aircraft. Annual Conference of the Prognostics and Health Management Society 2015.
Hogge, E., Quach, C., Vazquez, S. & Hill, B. (2011). A Data System for a Rapid Evaluation Class of Subscale Aerial Vehicle. NASA/TM-2011-217145.
Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th international symposium on aerospace/defense sensing, simulation, and controls (pp. 182-193)
Julier, S. & Uhlmann, J. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401-422.
Lin, X. & Stefanopoulou, A. (2015). Analytic bound on accuracy of battery state and parameter estimation. Journal of the Electrochemical Society, 162 (9) A1879-A1891.
Nelder, J. & Mead, R. (1965). A simplex method for function minimization. Computer Journal 1965; 7 (4), 308-313.
Patterson, N., German, B. J. & Moore, M. D. (2012). Performance analysis and design of on-demand electric aircraft concepts. 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM (p. 27). Reston, VA: AIAA.
Quach, C., Bole, B., Hogge, E., Vazquez, S., Daigle, M., Celaya, J., Weber, A. & Goebel, K. (2013). Battery charge depletion prediction on an electric aircraft. Annual Conference of the Prognostics and Health Management Society 2013.
Saha, B., Koshimoto, E., Quach, C., Hogge, E., Strom, T., Hill, B., Vasquez, S. & Goebel, K. (2011). Battery health management system for electric UAV’s. IEEE Aerospace Conference. Big Sky, MT: IEEE.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health (IJPHM), Vol. 1, 2010.
Saxena, A., Roychoudhury, I., Celaya, J., Saha B., Saha S., & Goebel, K. (2012). Requirements flowdown for prognostics and health management. Infotech@Aerospace, 2013. AIAA, Garden Grove, CA.
Saxena, A., Roychoudhury, I., Lin, W. & Goebel, K. (2013). Towards requirements in systems engineering for aerospace IVHM design. AIAA Conference, 2013. AIAA, Reston, VA.
Thunder Power RC, 2013 Safety instructions and warnings, revision 3 (December 10, 2013). www.ThunderPowerRC.com (2).
Wang, Y., Fang, H., Zhou, L. & Wada, T. (2017). Revisiting the state-of-charge estimation for lithium-ion batteries. IEEE Control Systems, Vol. 37 (4), 73-96. doi: 10.1109/MCS.2017.2696761
Zhang, H. & Chow, M.-Y. (2010). Comprehensive dynamic battery modeling for PHEV applications. Power and Energy Society General Meeting, 2010, IEEE. Minneapolis, MN: IEEE.
Section
Technical Papers