An improved model for remaining useful life prediction on capacity degradation and regeneration of lithium-ion battery

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 2, 2017
Li-Ming Deng Yu-Cheng Hsu Han-Xiong Li

Abstract

The regeneration phenomena of the lithium-ion battery are widely existed in reality but rarely studied due to the gap between experiment conditions and practical working conditions. In this paper, the capacity regeneration phenomena are considered during the degradation process of batteries. An improved empirical model incorporating both rest time and discharge cycles for remaining useful life (RUL) prediction is proposed. The degradation process and regeneration process have been described by different components and integrated to formulate the whole model. The dual estimation framework is employed to decouple the states and parameters during the degradation and regeneration process. The datasets from NASA Prognostics Center of Excellence (PCoE) have been adopted for model validation. The proposed model is compared with other empirical model and also different estimation methods. The results are satisfactory, and demonstrate the capability of the proposed model for the RUL prediction of Lithium-ion battery.

How to Cite

Deng, L.-M., Hsu, Y.-C., & Li, H.-X. (2017). An improved model for remaining useful life prediction on capacity degradation and regeneration of lithium-ion battery. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2438
Abstract 270 | PDF Downloads 277

##plugins.themes.bootstrap3.article.details##

Keywords

battery degradation, Battery Remaining Useful Life, dual extended kalman filter, regeneration phenomenon

References
Daigle, M., & Kulkarni, C. S. (2016). End-of-discharge and end-of-life prediction in lithium-ion batteries with electrochemistry-based aging models.
Eddahech, A., Briat, O., & Vinassa, J.-M. (2013). Lithiumion battery performance improvement based on capacity recovery exploitation. Electrochimica Acta, 114, 750–757.
Haug, A. J. (2012). Bayesian estimation and tracking: a practical guide. John Wiley & Sons.
He, W., Williard, N., Osterman, M., & Pecht, M. (2011). Prognostics of lithium-ion batteries based on dempster–shafer theory and the bayesian monte carlo method. Journal of Power Sources, 196(23), 10314–10321.
Huggins, R. (2008). Advanced batteries: materials science aspects. Springer Science & Business Media.
Jin, G., Matthews, D. E., & Zhou, Z. (2013). A bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft. Reliability Engineering & System Safety, 113, 7–20.
Lawson, B. (2005). Why batteries fail. http://www.mpoweruk.com/failure modes.htm. (Accessed: 2017-05-26)
Lopez, M. (2017). Samsung explains note 7 battery explosions, and turns crisis into opportunity. https://www.forbes.com/sites/maribellopez/2017/
01/22/samsung-reveals-cause-of-note-7-issue-turns-crisis-into-opportunity/#743f1f9b24f1. (Accessed: 2017-05-26)
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of power sources, 226, 272–288.
Najim, M. (2010). Modeling, estimation and optimal filtration in signal processing (Vol. 25). John Wiley & Sons.
Nazri, G.-A., & Pistoia, G. (2008). Lithium batteries: science and technology. Springer Science & Business Media.
Olivares, B. E., Munoz, M. A. C., Orchard, M. E., & Silva, J. F. (2013). Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena. IEEE Transactions on Instrumentation and Measurement, 62(2), 364–376.
Orchard, M., Tang, L., Saha, B., Goebel, K., & Vachtsevanos, G. (2010). Risk-sensitive particle-filtering-based prognosis framework for estimation of remaining useful life in energy storage devices. Studies in Informatics and Control, 19(3), 209–218.
Orchard, M. E., Lacalle, M. S., Olivares, B. E., Silva, J. F., Palma-Behnke, R., Estévez, P. A., . . . Cortés-Carmona, M. (2015). Information-theoretic measures and sequential monte carlo methods for detection of regeneration phenomena in the degradation of lithium-ion battery cells. IEEE Transactions on Reliability, 64(2), 701–709.
Orchard, M. E., & Vachtsevanos, G. J. (2009). A particlefiltering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31(3-4), 221–246.
Plett, G. L. (2004). Extended kalman filtering for battery management systems of lipb-based hev battery packs: Part 3. state and parameter estimation. Journal of Power sources, 134(2), 277–292.
Plett, G. L. (2005). Dual and joint ekf for simultaneous soc and soh estimation. In Cd-rom proceedings of the 21st electric vehicle symposium (evs21),(monaco: April 2005).
Qin, T., Zeng, S., & Guo, J. (2015). Robust prognostics for state of health estimation of lithium-ion batteries based on an improved pso–svr model. Microelectronics Reliability, 55(9), 1280–1284.
Qin, T., Zeng, S., Guo, J., & Skaf, Z. (2016). A rest timebased prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena. Energies, 9(11), 896.
Saha, B., & Goebel, K. (2007). Battery data set. https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/#battery. (Accessed: 2016-05-03)
Saha, B., & Goebel, K. (2009). Modeling li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society (pp. 2909–2924).
Tang, S., Yu, C., Wang, X., Guo, X., & Si, X. (2014). Remaining useful life prediction of lithium-ion batteries based on the wiener process with measurement error. Energies, 7(2), 520–547.
Wan, E. A., & Nelson, A. T. (2001). Dual extended kalman filter methods. Kalman filtering and neural networks, 123–173.
Wan, E. A., & Van Der Merwe, R. (2000). The unscented kalman filter for nonlinear estimation. In Adaptive systems for signal processing, communications, and control symposium 2000. as-spcc. the ieee 2000 (pp. 153–158).
Welch, G., & Bishop, G. (1995). An introduction to the kalman filter.
Xing, Y., Ma, E. W., Tsui, K.-L., & Pecht, M. (2013). An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability, 53(6), 811–820.
Yang, F.,Wang, D., Xing, Y., & Tsui, K.-L. (2017). Prognostics of L i(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model. Microelectronics Reliability, 70, 70–78.
Section
Technical Research Papers