Battery Capacity Estimation of Low-Earth Orbit Satellite Application
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Published
Oct 18, 2020
Myungsoo Jun
Kandler Smith
Eric Wood
Marshall.C. Smart
Abstract
Simultaneous estimation of the battery capacity and state- of-charge is a difficult problem because they are dependent on each other and neither is directly measurable. This paper proposes a particle filtering approach for the estimation of the battery state-of-charge and a statistical method to estimate the battery capacity. Two different methods and time scales have been used for this estimation in order to reduce the dependency on each other. The algorithms are validated using experimental data from A123 graphite/LiFePO4 lithium ion commercial-off-the-shelf cells, aged under partial depth-of- discharge cycling as encountered in low-earth-orbit satellite applications. The model-based method is extensible to bat- tery applications with arbitrary duty-cycles.
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Keywords
state of charge estimation, particle filter, lithium ion battery, capacity estimation
References
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Sanjeev Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 50(2), 174-188.
Santhanagopalan, S., Zhang, Q., Kumaresan, K., & White, R. E. (2008). Parameter estimation and life modeling of lithium-ion cells. J. Electrochem. Soc., 155(4), A345-A353.
Schulz, D., Burgard, W., Fox, D., & Cremers, A. (2001). Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In Proc. of the IEEE International Conference on Robotics and Automation (p. 1665-1670). Seoul, Korea.
Sheppard, J.W.,Wilmering, T. J., & Kaufman, M. A. (2009). IEEE Standards for Prognostics and Health Management. IEEE Aerospace and Electronic Systems Magazine, 24(9), 34-41.
Smith, K. (2010, April). Electrochemical Control of Lithium- Ion Batteries. IEEE Control Systems Magazine.
Smith, K., Rahn, C. D., & Wang, C. Y. (2007). Control- Oriented 1D Electrochemical Model of Lithium Ion Battery. Energy Conversion and Management, 48(9), 2565-2578.
Sun, F., Hu, X., Zou, Y., & Li, S. (2011). Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles. J. of Energy, 36, 3531-3540.
Verbrugge, M. W., & Koch, B. J. (2006). Generalized recursive algorithm for adaptive multiparameter regression. J. Electrochem. Soc., 153(1), A187-A201.
Vermaak, J., Andrieu, C., Doucet, A., & Godsill, S. (2002, March). Particle methods for Bayesian modeling and enhancement of speech signals. IEEE Trans. on Speech and Audio Processing, 10, 173-185.
Chen, H. (2012, july). Adaptive Cubature Kalman Filter for Nonlinear State and Parameter Estimation. In Proc. of 15th International Conference on Information Fusion. Singapore.
Danzer, M. A., & Hofer, E. P. (2008). Electrochemical parameter identification — An efficient method for fuel cell impedance characterization. J. Power Sources, 183, 55-61.
Goebel, K. (2010, October). System Health Management: Predicting Failure after Fielding a Product. In IEEE Accelerated Stress Testing and Reliability Workshop. Denver, CO.
Goebel, K., Saha, B., Saxena, A., Celaya, J. R., & Christophersen, J. (2008, August). Prognostics in Battery Health Management. IEEE Instrumentation & Measurement Magazine, 11(4), 33-40.
Hu, C., Youn, B. D., & Chung, J. (2012). A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation. J. of Applied Energy, 92, 694-704.
Isard, M., & Blake, A. (1998). CONDENSATION— conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1), 5-28.
Kim, J.,&Cho, B. H. (2011). State-of-Charge Estimation and State-of-Health Prediction of A Li-Ion Degraded Battery Based on An EKF Combined with A Per-Unit System. IEEE Trans. Vehicular Technology, 60(9), 4249-4260.
Kitagawa, G. (1996). Monte-Carlo filter and smoother for non-Gaussian nonlinear state space model. J. Comput. Graph. Statist., 1, 1-25.
Lee, J., Nam, O., & Cho, B. H. (2007). Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering. J. of Power Sources, 174, 9-15.
Lee, J. L., Chemistruck, A., & Plett, G. L. (2012, December). One-dimensional physics-based reduced-order model of lithium-ion dynamics. J. Power Sources, 220, 430448.
Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 3: State and parameter estimation. J. of Power Sources, 134(2), 277-292.
Plett, G. L. (2006). Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 2: Simultaneous state and parameter estimation. J. of Power Sources, 161, 1369-1384.
Plett, G. L. (2011). Recursive approximate weighted total least squares estimation of battery cell total capacity. J. of Power Sources, 196, 2319-2331.
Sanjeev Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 50(2), 174-188.
Santhanagopalan, S., Zhang, Q., Kumaresan, K., & White, R. E. (2008). Parameter estimation and life modeling of lithium-ion cells. J. Electrochem. Soc., 155(4), A345-A353.
Schulz, D., Burgard, W., Fox, D., & Cremers, A. (2001). Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In Proc. of the IEEE International Conference on Robotics and Automation (p. 1665-1670). Seoul, Korea.
Sheppard, J.W.,Wilmering, T. J., & Kaufman, M. A. (2009). IEEE Standards for Prognostics and Health Management. IEEE Aerospace and Electronic Systems Magazine, 24(9), 34-41.
Smith, K. (2010, April). Electrochemical Control of Lithium- Ion Batteries. IEEE Control Systems Magazine.
Smith, K., Rahn, C. D., & Wang, C. Y. (2007). Control- Oriented 1D Electrochemical Model of Lithium Ion Battery. Energy Conversion and Management, 48(9), 2565-2578.
Sun, F., Hu, X., Zou, Y., & Li, S. (2011). Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles. J. of Energy, 36, 3531-3540.
Verbrugge, M. W., & Koch, B. J. (2006). Generalized recursive algorithm for adaptive multiparameter regression. J. Electrochem. Soc., 153(1), A187-A201.
Vermaak, J., Andrieu, C., Doucet, A., & Godsill, S. (2002, March). Particle methods for Bayesian modeling and enhancement of speech signals. IEEE Trans. on Speech and Audio Processing, 10, 173-185.
Section
Technical Papers