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 forlithium-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 anddischarge 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.
Highlights
Over the last decades, we have experienced a significant increment in the development and production of electric vehicles
The weighting process is made by applying the sequential importance resampling (SIR) algorithm, which is explained in the following subsections
Even though the dynamics of the internal impedance are affected by several variables, all the results presented in this article are based on the state transition model described by Eqs. (18)-(19)
Summary
We have experienced a significant increment in the development and production of electric vehicles. The concept of “Battery Management Systems” (BMS) (Pattipati, Sankavaram, & Pattipati, 2011) rises naturally when looking for systems capable of providing protection and optimal operating conditions for batteries, while simultaneously accounting for life predictions through the supervision of real-time acquired data In this regard, the “State-of-Charge” (SOC) (Pattipati et al, 2011) -a measure of the remaining available energy stored-, the “State-ofHealth” (SOH) (Pattipati et al, 2011) -a measure of battery degradation-, and the associated “Remaining Useful Life” (RUL) (Orchard & Vachtsevanos, 2009) are parameters that provide important information about the current condition of the battery. INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT of multiple imputations was proposed in (Rubin, 1987) This latter method considers different values for each missing datum and combines their induced probability distributions into a single solution for parameter estimation.
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