Multiple-imputation-particle-filtering for Uncertainty Characterization in Battery State-of-Charge Estimation Problems with Missing Measurement Data: Performance Analysis and Impact on Prognostic Algorithms



Published Nov 3, 2020
David E. Acuña Marcos E. Orchard Jorge F. Silva Aramis Pérez


The implementation of particle-filtering-based algorithms for state estimation purposes often has to deal with the problem of missing observations. An efficient design requires an appropriate methodology for real-time uncertainty characterization within the estimation process, incorporating knowledge from other available sources of information. This article analyzes this problem and presents preliminary results for a multiple imputation strategy that improves the performance of particle-filtering-based state-of-charge (SOC) estimators for
lithium-ion (Li-Ion) battery cells. The proposed uncertainty characterization scheme is tested, and validated, in a case study where the state-space model requires both voltage and
discharge current measurements to estimate the SOC. A sudden disconnection of the battery voltage sensor is assumed to cause significant loss of data. Results show that the multipleimputation particle filter allows reasonable characterization of uncertainty bounds for state estimates, even when the voltage sensor disconnection continues. Furthermore, if voltage measurements are once more available, the uncertainty bounds adjust to levels that are comparable to the case where data were not lost. As state estimates are used as initial conditions for battery End-of-Discharge (EoD) prognosis modules, we also studied how these multiple-imputation algorithms impact on the quality of EoD estimates.

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particle filtering, state of charge estimation, multiple imputations

Acuña, D., Orchard, M., Silva, J., & Pérez, A. (2014). Multiple-imputation-particle-filtering scheme for uncertainty characterization in battery state-of-charge estimation problems with missing measurement data. Annual Conference of the Prognostics and Health Management Society 2014, 5(037), 1-9.
Andrieu, C., Doucet, A., & Punskaya, E. (2001). Sequential monte carlo methods in practice (A. Doucet, N. de Freitas, & N. Gordon, Eds.). Springer-Verlag.
Candy, J. (2009). Bayesian signal processing: Classical, modern and particle filtering methods. Wiley.
Crisan, D., & Doucet, A. (2002). A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing, 50(3), 736-746.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10(2), 197-208.
Graham, J., Olchowski, A., & Gilreath, T. (2007). How many imputations are really needed? some practical clarifications of multiple imputation theory. Prevention Science, 8, 206-213.
Housfater, A., Zhang, X., & Zhou, Y. (2006). Nonlinear fusion of multiple sensors with missing data. IEEE International Conference on Acoustics, Speech and Signal Processing, 4, 961-964.
Liu, J., Kong, A.,&Wong,W. (1994). Sequential imputations and bayesian missing data problems. Journal of the American Statistical Association, 89(425), 278-288.
Orchard, M., Cerda, M., Olivares, B., & Silva, J. (2012). Sequential monte carlo methods for discharge time prognosis in lithium-ion batteries. International Journal of Prognostics and Health Management, 3, 1-12.
Orchard, M., & Vachtsevanos, G. (2009). A particle-filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31, 221-246.
Pattipati, B., Sankavaram, C., & Pattipati, K. (2011). System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(6), 869-884.
Pola, D. (2014). An improved prognosis strategy with temperature-dependent state space models for the analysis of the state-of-health and state-of-charge in lithium-ion batteries. M.Sc. Tesis. Department of Electrical Engineering, Universidad de Chile.
Pola, D., Navarrete, H., Orchard, M., Rabié, R., Cerda, M., Olivares, B., & Silva, J. (2015). Particle-filteringbased discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles. IEEE Transactions on Reliability, 1-11.
Rubin, D. (1987). Multiple imputation for nonresponse in surveys. Wiley.
Schafer, J. (1997). Analysis of incomplete multivariate data. Chapman & Hall/CRC.
Zhang, X., Khwaja, A., Luo, J., & Housfater, A. (2014). Convergence analysis of multiple imputations particle filters for dealing with missing data in nonlinear problems. IEEE International Symposium on Circuits and Systems (ISCAS)(037), 2567-2570.
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