SIL/HIL Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Software-in-the-loop and hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The offline testing framework is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.
How to Cite
##plugins.themes.bootstrap3.article.details##
verification and validation, Battery discharge prognostics, SIL/HIL testing, unmanned aerial vehicle
Balaban, E., Saxena, A., Narasimhan, S., Roychoudhury, I., Goebel, K., & Koopmans, M. (2010). Airborne electro-mechanical actuator test stand for development of prognostic health management systems. In Annual conference of the prognostics and health management society.
Bole, B., Goebel, K., & Vachtsevanos, G. (2012). Stochastic modeling of component fault growth over a derived domain of fiesible output control effort modifications. In Annual conference of the prognostics and health management society.
Brown, A., & Garcia, R. (2009). Concepts and validation of a small-scale rotorcraft proportional integral derivative (pid) controller in a unique simulation environment. Unmanned Aircraft Systems, 1, 511-532.
Bukov, V., Chernyshov, V., Kirk, B., & Schagaev, I. (2007). Principle of active system safety for aviation: Challenges, supportive theory, implementation, application and future. In ASTEC.
Celaya, J., Kulkarni, C., Biswas, G., & Goebel, K. (2011). A model-based prognostics methodology for electrolytic capacitors based on electrical overstress accelerated aging. In Proceedings of the annual conference of the PHM society.
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., Saxena, A., & Goebel, K. (2012). An efficient deterministic approach to model-based prediction un- certainty. In Annual conference of the prognostics and health management society.
Gobbato, M., Conte, J., Kosmatka, J., & Farrar, C. (2012). A reliability-based framework for fatigue damage prognosis of composite aircraft structures. Probabilistic Engineering Mechanics, 29, 176-188.
Guidaa, M., & Pulcini, G. (2011). A continuous-state Markov model for age- and state-dependent degradation pro- cesses. Structural Safety, 33(6), 354-366.
Ibeiro, L., & Oliveira, N. M. (2010). UAV autopilot controllers test platform using MATLAB/Simulink and X- Plane. In Frontiers in education conference (FIE).
Jossen, A. (2006). Fundamentals of battery dynamics. Journal of Power Sources, 154, 530-538.
Kelly, K., Mihalic, M., & Zolot, M. (2002). Battery usage and thermal performance of the toyota prius and honda insight during chassis dynamometer testing. In Seventeenth annual IEEE battery conference on applications and advances.
Lopez, I., & Sarigul-Klijn, N. (2010). A review of uncertainty in flight vehicle structural damage monitoring, diagnosis and control: Challenges and opportunities. Progress in Aerospace Sciences, 46, 247-273.
Napolitano, M., Windon, D., Casanova, J., Innocenti, M., & Silvestri, G. (1998). Kalman filters and neural-network schemes for sensor validation in flight control systems. IEEE Transactions on Control Systems Technology.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.