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
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Abstract
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
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