Battery Charge Depletion Prediction on an Electric Aircraft



Quach Cuong Chi Brian Bole Edward Hogge Sixto Vazquez Matthew Daigle Jose ́ Celaya Adam Weber Kai Goebel


Validation of prognostic technologies through ground and flight tests is an important step in maturing these novel technologies and deploying them on real-world systems. To this end, a series of flight tests have been conducted using an un- manned electric vehicle during which the motor system batteries were monitored by a prognostic algorithm. The research presented here endeavors to produce and validate a technology for predicting the remaining time until end-of- discharge of the batteries on an electric aircraft as a function of an expected future flight and online estimates of the charge contained in the batteries. Flight data and flight experiment results are presented along with an assessment of model and algorithm performance.

How to Cite

Cuong Chi, Q. ., 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 PHM Society, 5(1).
Abstract 131 | PDF Downloads 418



Battery discharge prognostics, unmanned aerial vehicle, Electric Aircraft, Kalman Filtering

Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.

Bole, B., Teubert, C., Chi, Q. C., Edward, H., 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. In Annual conference of the prognostics and health management 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., & Goebel, K. (2013, May). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.

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.

Harrup, P., & Davis, S. (2010). 21st-century technology propels electric aircraft into the “blue yonder”. Power Electronics Technology, 36, 16-21.

Hogge, E. F., Quach, C. C., & Hill, B. L. (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. J., & Uhlmann, J. K. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.

Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008, September). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 38(5), 1156 -1168.

Orchard, M., Tobar, F., & Vachtsevanos, G. (2009, December). Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical per- formance comparison. Studies in Informatics and Control, 18(4), 295-304.

Orchard, M. E., Cerda, M. A., Olivares, B. E., & Silva, J. F. (2012). Sequential Monte Carlo methods for discharge time prognosis in Lithium-Ion batteries. International Journal of Prognostics and Health Management, 3, 1- 12.

Pang, S., Farrell, J., Du, J., & Barth, M. (2001). Battery state- of-charge estimation. In American control conference.

Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. In IEEE aerospace conference.

Saha, B., & Goebel, K. (2009, September). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society 2009.

Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009). Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291-296.

Saha, B., Koshimoto, E., Quach, C. C., Hogge, E. F., Strom, T. H., Hill, B. L., . . . Goebel, K. (2011). Battery health management system for electric UAVs. In IEEE aerospace conference.

Zhang, H., & Chow, M.-Y. (2010). Comprehensive dynamic battery modeling for PHEV applications. In IEEE power and energy society general meeting.
Technical Papers

Most read articles by the same author(s)

<< < 1 2 3 4 > >>