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Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena

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4 Author(s)
Olivares, B.E. ; Dept. of Electr. Eng., Univ. de Chile, Santiago, Chile ; Cerda Munoz, M.A. ; Orchard, M.E. ; Silva, J.F.

This paper presents the implementation of a particle-filtering-based prognostic framework that allows estimating the state of health (SOH) and predicting the remaining useful life (RUL) of energy storage devices, and more specifically lithium-ion batteries, while simultaneously detecting and isolating the effect of self-recharge phenomena within the life-cycle model. The proposed scheme and the statistical characterization of capacity regeneration phenomena are validated through experimental data from an accelerated battery degradation test and a set of ad hoc performance measures to quantify the precision and accuracy of the RUL estimates. In addition, a simplified degradation model is presented to analyze and compare the performance of the proposed approach in the case where the optimal solution (in the mean-square-error sense) can be found analytically.

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Instrumentation and Measurement, IEEE Transactions on  (Volume:62 ,  Issue: 2 )