Electrochemistry-based Battery Modeling for Prognostics
Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commercial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.
How to Cite
lithium-ion batteries, model-based prognostics
Chen, M., & Rincon-Mora, G. (2006, June). Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion, 21(2), 504 - 511.
Daigle, M., Bregon, A., & Roychoudhury, I. (2012, Septem- ber). A distributed approach to system-level prognostics. In Annual conference of the prognostics and health management society (p. 71-82).
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., Saha, B., & Goebel, K. (2012, March). A comparison of filter-based approaches for model- based prognostics. In Proceedings of the 2012 IEEE aerospace conference.
Daigle, M., Saxena, A., & Goebel, K. (2012, Septem- ber). An efficient deterministic approach to model- based prediction uncertainty estimation. In Annual conference of the prognostics and health management society (p. 326-335).
Doyle, M., Fuller, T. F., & Newman, J. (1993, June). Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical Society, 140(6), 1526-1533.
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, mar). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
Karthikeyan, D. K., Sikha, G., & White, R. E. (2008). Thermodynamic model development for lithium intercalation electrodes. Journal of Power Sources, 185(2), 1398–1407.
Lee, K. J., Smith, K., Pesaran, A., & Kim, G. H. (2013, November). Three dimensional thermal-, electrical-, and electrochemical-coupled model for cylindrical wound large format lithium-ion batteries. Journal of Power Sources(1), 20-32.
Newman, J., & Tiedemann, W. (1975). Porous-electrode theory with battery applications. AIChE Journal, 21(1), 25-41.
Ning, G., & Popov, B. N. (2004). Cycle life modeling of lithium-ion batteries. Journal of The Electrochemical Society, 151(10), A1584–A1591.
Rahn, C. D., & Wang, C.-Y. (2013). Battery systems engineering. Wiley.
Ramadesigan, V., Northrop, P. W., De, S., Santhanagopalan, S., Braatz, R. D., & Subramanian, V. R. (2012). Modeling and simulation of lithium-ion batteries from a systems engineering perspective. Journal of The Electrochemical Society, 159(3), R31–R45.
Rong, P., & Pedram, M. (2006). An analytical model for predicting the remaining battery capacity of lithium-ion batteries. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on, 14(5), 441–451.
Ross, P. (2013). Boeing’s battery blues. IEEE Spectrum, 50(3), 11-12.
Saha, B., & Goebel, K. (2009, September). Modeling Li-ion battery capacity depletion in a particle filtering frame- work. In Proceedings of the annual conference of the prognostics and health management society 2009.
Sankararaman, S., Daigle, M., Saxena, A., & Goebel, K. (2013, March). Analytical algorithms to quantify the uncertainty in remaining useful life prediction. In Proceedings of the 2013 IEEE aerospace conference.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1).
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.