Lithium-Ion Battery End-of-Discharge Time Estimation and Prognosis based on Bayesian Algorithms and Outer Feedback Correction Loops: A Comparative Analysis



Published Oct 18, 2015
Carlos Tampier Aramis Pérez Francisco Jaramillo Vanessa Quintero Marcos E. Orchard Jorge F. Silva


Battery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs.

How to Cite

Tampier, C. ., Pérez, A., Jaramillo, F., Quintero, V. ., E. Orchard, M. ., & F. Silva, J. . (2015). Lithium-Ion Battery End-of-Discharge Time Estimation and Prognosis based on Bayesian Algorithms and Outer Feedback Correction Loops: A Comparative Analysis. Annual Conference of the PHM Society, 7(1).
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unscented Kalman filter, particle filter, Battery discharge prognostics

Bole, B. D. (2014). Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft. Second European Conference of the Prognostics and Health Management Society 2014, 23-32.

Cadar, D. P. (2009). method of determining a lithium-ion battery's state of charge. de 2009 15th International Symposium for Design and Technology of Electronics Packages (SIITME).

Cerda, M. (2012). Estimación en línea del tiempo de descarga de baterías de ion-litio utilizando caracterización del perfil de utilización y métodos secuenciales de Monte Carlo. M.S. Thesis, Universidad de Chile.

Charkhgard, M. &. (2010). State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE Trans. on Industrial Electronics , vol. 57, no 12,, 4178-4187.

Cruse, T. (2004). Probabilistic Systems Modeling and Validation. HCF.

Dalal, M. M. (2011). Lithium-ion battery life prognostic health management system using particle filtering framework. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 225, no 1, 81-90.

Di Z., Y. M.-W. (2011). Estimation of Lithium-ion battery state of charge. 30th Chinese Control Conference (CCC).

He, W. W. (2011). Remaining useful performance analysis of batteries. IEEE Conference on Prognostics and Health Management (PHM).

Orchard, M. (2007). A Particle Filtering- based Framework for On-line Fault Diagnosis and Failure Prognosis. Ph.D. Thesis, Department of Electrical and Computer Engineering, Georgia Institute of Technology.

Orchard, M. K. (2008). Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics. International Conference on Prognostics and Health Management PHM 2008.

Orchard, M. T. (2009). Outer Feedback Correction Loops in Particle Filtering-based Prognostic Algorithms: Statistical Performance Comparison. Studies in Informatics and Control, vol. 18, no 4, 295-304.

Orchard, M. T. (2010). Risk-sensitive particle-filtering- based prognosis framework for estimation of remaining useful life in energy storage devices. Studies in Informatics and Control, vol. 19, no 3, 209-218.

Pattipati, B. S. (2011). System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Trans.
on Systems, Man, and Cybernetics, Part C:

Applications and Reviews, vol. 41, no 6,, 869-884. Pola, D. N. (2014). Particle-filtering-based Discharge Time Prognosis for Lithium-Ion Batteries with a Statistical Characterization of Use Profiles. IEEE Transactions on Reability, 1-11.

Qingsheng, S. C. (2010). Battery State-Of-Charge estimation in Electric Vehicle using Elman neural network method. 29th Chinese Control Conference (CCC).

Ran, L. J. (2010). Prediction of state of charge of lithium- ion rechargeable battery with electrochemical impedance spectroscopy theory. the 5th IEEE Conference on Industrial Electronics and Applications.

Ranjbar, A. H. (2012). Online Estimation of State of Charge in Li-Ion Batteries Using Impulse Response Concept. IEEE Trans. on Smart Grid, vol. 3, no 1,, 360-367.

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

Saha, B. G. (2009). Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, vol. 58, no 2, 291-296.

Tang, X. M. (2011). Li-ion battery parameter estimation for state of charge. American Control Conference (ACC).

Van Der Merwe, R. &. (2001). The square-root unscented Kalman filter for state and parameter-estimation. Acoustics, Speech, and Signal Processing. IEEE International Conference on , 3461- 3464 vol.6 .
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